{
  "nodes": [
    {
      "id": "bayesian/bayes-factors",
      "title": "Bayes Factors",
      "url": "tutorials/bayesian/bayes-factors.html",
      "topic": "bayesian",
      "tags": [
        "bayes-factor",
        "model-comparison",
        "marginal-likelihood",
        "topic:bayesian-statistics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Ratio of marginal likelihoods under two models, the Bayesian analogue of a likelihood-ratio test"
    },
    {
      "id": "bayesian/bayes-hypothesis-testing",
      "title": "Bayesian Hypothesis Testing",
      "url": "tutorials/bayesian/bayes-hypothesis-testing.html",
      "topic": "bayesian",
      "tags": [
        "hypothesis-testing",
        "rope",
        "bayes-factor",
        "topic:bayesian-statistics",
        "bayesian-methods",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Region of practical equivalence (ROPE) and Bayes factors for Bayesian decisions"
    },
    {
      "id": "bayesian/bayes-theorem-for-parameters",
      "title": "Bayes' Theorem for Parameters",
      "url": "tutorials/bayesian/bayes-theorem-for-parameters.html",
      "topic": "bayesian",
      "tags": [
        "bayes-theorem",
        "posterior",
        "prior",
        "likelihood",
        "topic:bayesian-statistics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Using Bayes' theorem to update beliefs about parameters given data"
    },
    {
      "id": "bayesian/bayesian-anova",
      "title": "Bayesian ANOVA",
      "url": "tutorials/bayesian/bayesian-anova.html",
      "topic": "bayesian",
      "tags": [
        "bayesian-anova",
        "contrast",
        "group-comparison",
        "topic:bayesian-statistics",
        "hypothesis-testing",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Posterior inference on group differences with credible intervals and contrast comparisons"
    },
    {
      "id": "bayesian/bayesian-hierarchical-intro",
      "title": "Bayesian Hierarchical Models",
      "url": "tutorials/bayesian/bayesian-hierarchical-intro.html",
      "topic": "bayesian",
      "tags": [
        "hierarchical",
        "multilevel",
        "partial-pooling",
        "shrinkage",
        "topic:bayesian-statistics",
        "bayesian-methods",
        "longitudinal-and-mixed-models"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Partial pooling through prior structure for grouped / multilevel data"
    },
    {
      "id": "bayesian/bayesian-mediation",
      "title": "Bayesian Mediation Analysis",
      "url": "tutorials/bayesian/bayesian-mediation.html",
      "topic": "bayesian",
      "tags": [
        "mediation",
        "indirect-effect",
        "bayesian",
        "topic:bayesian-statistics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Testing indirect effects with posterior samples"
    },
    {
      "id": "bayesian/bayesian-meta-analysis",
      "title": "Bayesian Meta-Analysis",
      "url": "tutorials/bayesian/bayesian-meta-analysis.html",
      "topic": "bayesian",
      "tags": [
        "bayesian-meta-analysis",
        "random-effects",
        "tau",
        "topic:bayesian-statistics",
        "bayesian-methods",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Hierarchical meta-analysis with explicit prior on between-study heterogeneity"
    },
    {
      "id": "bayesian/bayesian-mixed-models",
      "title": "Bayesian Mixed Models",
      "url": "tutorials/bayesian/bayesian-mixed-models.html",
      "topic": "bayesian",
      "tags": [
        "bayesian-mixed",
        "multilevel",
        "random-effects",
        "topic:bayesian-statistics",
        "regression",
        "bayesian-methods",
        "longitudinal-and-mixed-models",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Multilevel regression with random effects, fit via Bayesian MCMC"
    },
    {
      "id": "bayesian/beta-binomial-model",
      "title": "The Beta-Binomial Model",
      "url": "tutorials/bayesian/beta-binomial-model.html",
      "topic": "bayesian",
      "tags": [
        "beta-binomial",
        "conjugate",
        "proportion",
        "topic:bayesian-statistics",
        "bayesian-methods",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Conjugate Bayesian inference for a binomial proportion with a Beta prior"
    },
    {
      "id": "bayesian/brms-basics",
      "title": "brms Basics",
      "url": "tutorials/bayesian/brms-basics.html",
      "topic": "bayesian",
      "tags": [
        "brms",
        "stan",
        "bayesian-regression",
        "topic:bayesian-statistics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "High-level R interface to Stan: formula syntax, families, prior specification"
    },
    {
      "id": "bayesian/conjugate-priors",
      "title": "Conjugate Priors",
      "url": "tutorials/bayesian/conjugate-priors.html",
      "topic": "bayesian",
      "tags": [
        "bayesian",
        "conjugate-priors",
        "beta-binomial",
        "posterior",
        "prior-selection",
        "topic:bayesian-statistics",
        "bayesian-methods",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "When the prior and posterior share the same distributional family: the beta-binomial, normal-normal, and gamma-Poisson models"
    },
    {
      "id": "bayesian/credible-intervals",
      "title": "Credible Intervals",
      "url": "tutorials/bayesian/credible-intervals.html",
      "topic": "bayesian",
      "tags": [
        "credible-interval",
        "hpd",
        "equal-tailed",
        "topic:bayesian-statistics",
        "bayesian-methods",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Bayesian analogue of confidence intervals: equal-tailed and highest-posterior-density regions"
    },
    {
      "id": "bayesian/divergences-pairs-plots",
      "title": "Divergences and Pairs Plots",
      "url": "tutorials/bayesian/divergences-pairs-plots.html",
      "topic": "bayesian",
      "tags": [
        "divergence",
        "hmc",
        "pairs-plot",
        "funnel",
        "topic:bayesian-statistics",
        "bayesian-methods",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Diagnosing HMC sampling problems via divergent transitions and pair-plot visualisations"
    },
    {
      "id": "bayesian/gamma-poisson-model",
      "title": "The Gamma-Poisson Model",
      "url": "tutorials/bayesian/gamma-poisson-model.html",
      "topic": "bayesian",
      "tags": [
        "gamma-poisson",
        "conjugate",
        "rate",
        "topic:bayesian-statistics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Conjugate Bayesian inference for a Poisson rate with Gamma prior"
    },
    {
      "id": "bayesian/gibbs-sampling",
      "title": "Gibbs Sampling",
      "url": "tutorials/bayesian/gibbs-sampling.html",
      "topic": "bayesian",
      "tags": [
        "gibbs",
        "mcmc",
        "conditional",
        "topic:bayesian-statistics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "MCMC via sequential sampling from conditional distributions"
    },
    {
      "id": "bayesian/hamiltonian-monte-carlo",
      "title": "Hamiltonian Monte Carlo",
      "url": "tutorials/bayesian/hamiltonian-monte-carlo.html",
      "topic": "bayesian",
      "tags": [
        "hmc",
        "nuts",
        "gradient-mcmc",
        "topic:bayesian-statistics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Gradient-based MCMC using Hamiltonian dynamics; NUTS variant in Stan"
    },
    {
      "id": "bayesian/hpd-interval",
      "title": "Highest Posterior Density Interval",
      "url": "tutorials/bayesian/hpd-interval.html",
      "topic": "bayesian",
      "tags": [
        "hpd",
        "credible-interval",
        "posterior",
        "topic:bayesian-statistics",
        "bayesian-methods",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Shortest interval containing a specified posterior probability mass"
    },
    {
      "id": "bayesian/informative-priors",
      "title": "Informative Priors",
      "url": "tutorials/bayesian/informative-priors.html",
      "topic": "bayesian",
      "tags": [
        "informative-priors",
        "expert-elicitation",
        "meta-analysis",
        "topic:bayesian-statistics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Priors derived from previous studies, expert elicitation, or mechanistic knowledge"
    },
    {
      "id": "bayesian/jeffreys-prior",
      "title": "Jeffreys' Prior",
      "url": "tutorials/bayesian/jeffreys-prior.html",
      "topic": "bayesian",
      "tags": [
        "jeffreys",
        "reference-prior",
        "invariance",
        "topic:bayesian-statistics",
        "hypothesis-testing",
        "bayesian-methods",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Invariant reference prior proportional to the square root of the Fisher information"
    },
    {
      "id": "bayesian/linear-regression-bayes",
      "title": "Bayesian Linear Regression",
      "url": "tutorials/bayesian/linear-regression-bayes.html",
      "topic": "bayesian",
      "tags": [
        "bayesian-regression",
        "brms",
        "rstanarm",
        "topic:bayesian-statistics",
        "regression",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Linear regression with priors on coefficients, fit via MCMC in brms or rstanarm"
    },
    {
      "id": "bayesian/logistic-regression-bayes",
      "title": "Bayesian Logistic Regression",
      "url": "tutorials/bayesian/logistic-regression-bayes.html",
      "topic": "bayesian",
      "tags": [
        "bayesian-logistic",
        "binary",
        "brms",
        "topic:bayesian-statistics",
        "regression",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Logistic regression with MCMC-based posterior for binary outcomes"
    },
    {
      "id": "bayesian/loo-cv",
      "title": "Leave-One-Out Cross-Validation",
      "url": "tutorials/bayesian/loo-cv.html",
      "topic": "bayesian",
      "tags": [
        "loo",
        "psis",
        "cross-validation",
        "model-comparison",
        "topic:bayesian-statistics",
        "bayesian-methods",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Efficient Bayesian LOO-CV via Pareto-smoothed importance sampling (PSIS-LOO)"
    },
    {
      "id": "bayesian/metropolis-hastings",
      "title": "Metropolis-Hastings",
      "url": "tutorials/bayesian/metropolis-hastings.html",
      "topic": "bayesian",
      "tags": [
        "metropolis-hastings",
        "mcmc",
        "proposal",
        "topic:bayesian-statistics",
        "bayesian-methods",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The foundational MCMC algorithm with proposal distribution and accept/reject rule"
    },
    {
      "id": "bayesian/normal-normal-model",
      "title": "The Normal-Normal Model",
      "url": "tutorials/bayesian/normal-normal-model.html",
      "topic": "bayesian",
      "tags": [
        "normal-normal",
        "conjugate",
        "mean",
        "topic:bayesian-statistics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Conjugate Bayesian inference for a normal mean with known variance"
    },
    {
      "id": "bayesian/posterior-mean-variance",
      "title": "Posterior Mean and Variance",
      "url": "tutorials/bayesian/posterior-mean-variance.html",
      "topic": "bayesian",
      "tags": [
        "posterior",
        "point-estimate",
        "posterior-mean",
        "topic:bayesian-statistics",
        "bayesian-methods",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Point estimates and uncertainty summaries from a posterior distribution"
    },
    {
      "id": "bayesian/posterior-predictive-checks",
      "title": "Posterior Predictive Checks",
      "url": "tutorials/bayesian/posterior-predictive-checks.html",
      "topic": "bayesian",
      "tags": [
        "pp-check",
        "model-criticism",
        "posterior-predictive",
        "topic:bayesian-statistics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Comparing simulated data from the fitted model to observed data for model criticism"
    },
    {
      "id": "bayesian/posterior-predictive-distribution",
      "title": "The Posterior Predictive Distribution",
      "url": "tutorials/bayesian/posterior-predictive-distribution.html",
      "topic": "bayesian",
      "tags": [
        "posterior-predictive",
        "prediction",
        "ppd",
        "topic:bayesian-statistics",
        "bayesian-methods",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Distribution of a new observation integrating over posterior uncertainty about parameters"
    },
    {
      "id": "bayesian/prior-specification",
      "title": "Prior Specification",
      "url": "tutorials/bayesian/prior-specification.html",
      "topic": "bayesian",
      "tags": [
        "prior",
        "weakly-informative",
        "informative",
        "topic:bayesian-statistics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Choosing priors: informative, weakly informative, flat, and the risks of each"
    },
    {
      "id": "bayesian/rhat-ess-diagnostics",
      "title": "R-hat and ESS Diagnostics",
      "url": "tutorials/bayesian/rhat-ess-diagnostics.html",
      "topic": "bayesian",
      "tags": [
        "rhat",
        "ess",
        "convergence",
        "autocorrelation",
        "topic:bayesian-statistics",
        "power-and-sample-size",
        "bayesian-methods",
        "asymptotic-theory"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Convergence (R-hat) and effective sample size (ESS) for MCMC quality control"
    },
    {
      "id": "bayesian/rstanarm",
      "title": "rstanarm",
      "url": "tutorials/bayesian/rstanarm.html",
      "topic": "bayesian",
      "tags": [
        "rstanarm",
        "stan",
        "bayesian-glm",
        "topic:bayesian-statistics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Pre-compiled Stan models with a frequentist-like R interface"
    },
    {
      "id": "bayesian/savage-dickey-ratio",
      "title": "The Savage-Dickey Density Ratio",
      "url": "tutorials/bayesian/savage-dickey-ratio.html",
      "topic": "bayesian",
      "tags": [
        "savage-dickey",
        "bayes-factor",
        "nested-null",
        "topic:bayesian-statistics",
        "bayesian-methods",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Bayes factor for a point null via posterior/prior density ratio"
    },
    {
      "id": "bayesian/stan-introduction",
      "title": "Stan: Introduction",
      "url": "tutorials/bayesian/stan-introduction.html",
      "topic": "bayesian",
      "tags": [
        "stan",
        "rstan",
        "cmdstanr",
        "topic:bayesian-statistics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Writing probabilistic models in Stan's data-parameters-model blocks"
    },
    {
      "id": "bayesian/tidybayes-workflow",
      "title": "tidybayes Workflow",
      "url": "tutorials/bayesian/tidybayes-workflow.html",
      "topic": "bayesian",
      "tags": [
        "tidybayes",
        "posterior",
        "ggplot2",
        "topic:bayesian-statistics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Extract, manipulate, and visualise posterior draws in tidyverse style"
    },
    {
      "id": "bayesian/waic",
      "title": "WAIC",
      "url": "tutorials/bayesian/waic.html",
      "topic": "bayesian",
      "tags": [
        "waic",
        "information-criterion",
        "model-comparison",
        "topic:bayesian-statistics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Widely applicable information criterion: Bayesian out-of-sample prediction score"
    },
    {
      "id": "bayesian/weakly-informative-priors",
      "title": "Weakly Informative Priors",
      "url": "tutorials/bayesian/weakly-informative-priors.html",
      "topic": "bayesian",
      "tags": [
        "weakly-informative",
        "regularisation",
        "prior",
        "topic:bayesian-statistics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Priors that regularise without imposing substantive constraints"
    },
    {
      "id": "bioinformatics/annotation-variant-vep",
      "title": "Variant Annotation with VEP",
      "url": "tutorials/bioinformatics/annotation-variant-vep.html",
      "topic": "bioinformatics",
      "tags": [
        "VEP",
        "annotation",
        "consequence",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Predicting functional consequences of variants using Ensembl VEP"
    },
    {
      "id": "bioinformatics/atac-seq-analysis",
      "title": "ATAC-Seq Analysis",
      "url": "tutorials/bioinformatics/atac-seq-analysis.html",
      "topic": "bioinformatics",
      "tags": [
        "atac-seq",
        "chromatin",
        "accessibility",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Chromatin accessibility profiling via ATAC-seq peak calling and differential analysis"
    },
    {
      "id": "bioinformatics/batch-correction-combat",
      "title": "Batch Correction with ComBat",
      "url": "tutorials/bioinformatics/batch-correction-combat.html",
      "topic": "bioinformatics",
      "tags": [
        "batch",
        "combat",
        "sva",
        "topic:bioinformatics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Removing known batch effects from expression data using empirical-Bayes methods"
    },
    {
      "id": "bioinformatics/biomart-annotation",
      "title": "Gene Annotation with biomaRt",
      "url": "tutorials/bioinformatics/biomart-annotation.html",
      "topic": "bioinformatics",
      "tags": [
        "biomart",
        "annotation",
        "ensembl",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Programmatic queries against Ensembl for IDs, coordinates, and annotations"
    },
    {
      "id": "bioinformatics/bulk-rnaseq-differential-expression",
      "title": "Bulk RNA-seq Differential Expression with DESeq2",
      "url": "tutorials/bioinformatics/bulk-rnaseq-differential-expression.html",
      "topic": "bioinformatics",
      "tags": [
        "rna-seq",
        "deseq2",
        "differential-expression",
        "bioconductor",
        "negative-binomial",
        "topic:bioinformatics",
        "omics-and-genomics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "A complete DESeq2 workflow: from count matrix through normalisation, dispersion estimation, Wald testing, and LFC shrinkage"
    },
    {
      "id": "bioinformatics/bwa-bowtie-alignment",
      "title": "Alignment with BWA and Bowtie",
      "url": "tutorials/bioinformatics/bwa-bowtie-alignment.html",
      "topic": "bioinformatics",
      "tags": [
        "bwa",
        "bowtie2",
        "alignment",
        "bwt",
        "topic:bioinformatics",
        "omics-and-genomics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Fast read alignment to a reference genome via Burrows-Wheeler Transform indexing"
    },
    {
      "id": "bioinformatics/chip-seq-analysis",
      "title": "ChIP-Seq Analysis",
      "url": "tutorials/bioinformatics/chip-seq-analysis.html",
      "topic": "bioinformatics",
      "tags": [
        "chip-seq",
        "peaks",
        "motifs",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "TF binding and histone modification profiling from ChIP-seq data"
    },
    {
      "id": "bioinformatics/cnv-analysis",
      "title": "Copy Number Variation Analysis",
      "url": "tutorials/bioinformatics/cnv-analysis.html",
      "topic": "bioinformatics",
      "tags": [
        "cnv",
        "segmentation",
        "copy-number",
        "topic:bioinformatics",
        "omics-and-genomics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Inferring genomic copy-number changes from sequencing or array data"
    },
    {
      "id": "bioinformatics/counting-reads-feature-counts",
      "title": "Counting Reads with featureCounts",
      "url": "tutorials/bioinformatics/counting-reads-feature-counts.html",
      "topic": "bioinformatics",
      "tags": [
        "featurecounts",
        "htseq",
        "gene-counts",
        "topic:bioinformatics",
        "omics-and-genomics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Assigning reads to genes or genomic features from a BAM file"
    },
    {
      "id": "bioinformatics/drug-target-interaction",
      "title": "Drug-Target Interaction Mining",
      "url": "tutorials/bioinformatics/drug-target-interaction.html",
      "topic": "bioinformatics",
      "tags": [
        "drug",
        "target",
        "chembl",
        "pharmacology",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Integrating bioassay databases for drug-target identification"
    },
    {
      "id": "bioinformatics/edger-differential-expression",
      "title": "Differential Expression with edgeR",
      "url": "tutorials/bioinformatics/edger-differential-expression.html",
      "topic": "bioinformatics",
      "tags": [
        "edger",
        "differential-expression",
        "glmqlftest",
        "topic:bioinformatics",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "NB GLM-based differential expression with exactTest and quasi-likelihood F-test"
    },
    {
      "id": "bioinformatics/fastq-quality-control",
      "title": "FASTQ Quality Control",
      "url": "tutorials/bioinformatics/fastq-quality-control.html",
      "topic": "bioinformatics",
      "tags": [
        "fastq",
        "phred",
        "fastqc",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Inspecting sequencing read quality via Phred scores and FastQC-style reports"
    },
    {
      "id": "bioinformatics/gene-ontology-enrichment",
      "title": "Gene Ontology Enrichment",
      "url": "tutorials/bioinformatics/gene-ontology-enrichment.html",
      "topic": "bioinformatics",
      "tags": [
        "GO",
        "enrichment",
        "ORA",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Over-representation of GO terms in a differentially-expressed gene set"
    },
    {
      "id": "bioinformatics/gsea-preranked",
      "title": "GSEA Preranked Analysis",
      "url": "tutorials/bioinformatics/gsea-preranked.html",
      "topic": "bioinformatics",
      "tags": [
        "gsea",
        "fgsea",
        "ranked",
        "topic:bioinformatics",
        "hypothesis-testing"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Enrichment of gene sets in a ranked list without a significance cutoff"
    },
    {
      "id": "bioinformatics/gsva-single-sample",
      "title": "GSVA Single-Sample Enrichment",
      "url": "tutorials/bioinformatics/gsva-single-sample.html",
      "topic": "bioinformatics",
      "tags": [
        "gsva",
        "ssgsea",
        "single-sample",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Per-sample pathway scores for downstream sample-level analysis"
    },
    {
      "id": "bioinformatics/heatmaps-rnaseq",
      "title": "Heatmaps for RNA-seq",
      "url": "tutorials/bioinformatics/heatmaps-rnaseq.html",
      "topic": "bioinformatics",
      "tags": [
        "heatmap",
        "visualisation",
        "clustering",
        "topic:bioinformatics",
        "omics-and-genomics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Visualising expression patterns across genes and samples with clustering"
    },
    {
      "id": "bioinformatics/limma-voom",
      "title": "Differential Expression with limma-voom",
      "url": "tutorials/bioinformatics/limma-voom.html",
      "topic": "bioinformatics",
      "tags": [
        "limma",
        "voom",
        "precision-weights",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Transforming counts for linear modelling with precision weights"
    },
    {
      "id": "bioinformatics/ma-plots",
      "title": "MA Plots",
      "url": "tutorials/bioinformatics/ma-plots.html",
      "topic": "bioinformatics",
      "tags": [
        "visualisation",
        "ma-plot",
        "diagnostics",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Mean-average plots for differential expression diagnostics"
    },
    {
      "id": "bioinformatics/metagenomic-profiling",
      "title": "Metagenomic Profiling",
      "url": "tutorials/bioinformatics/metagenomic-profiling.html",
      "topic": "bioinformatics",
      "tags": [
        "metagenomics",
        "metaphlan",
        "kraken",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Whole-metagenome taxonomic and functional profiling with MetaPhlAn and Kraken"
    },
    {
      "id": "bioinformatics/methylation-analysis",
      "title": "DNA Methylation Analysis",
      "url": "tutorials/bioinformatics/methylation-analysis.html",
      "topic": "bioinformatics",
      "tags": [
        "methylation",
        "bisulfite",
        "minfi",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Differentially methylated positions and regions from bisulfite or array data"
    },
    {
      "id": "bioinformatics/microbiome-dada2",
      "title": "Microbiome Analysis with DADA2",
      "url": "tutorials/bioinformatics/microbiome-dada2.html",
      "topic": "bioinformatics",
      "tags": [
        "microbiome",
        "16S",
        "DADA2",
        "ASV",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Amplicon sequence variant (ASV) inference from 16S/ITS amplicon data"
    },
    {
      "id": "bioinformatics/microbiome-diversity",
      "title": "Microbiome Diversity Metrics",
      "url": "tutorials/bioinformatics/microbiome-diversity.html",
      "topic": "bioinformatics",
      "tags": [
        "diversity",
        "shannon",
        "unifrac",
        "microbiome",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Alpha and beta diversity measures for community comparison"
    },
    {
      "id": "bioinformatics/msa-biostrings",
      "title": "Multiple Sequence Alignment",
      "url": "tutorials/bioinformatics/msa-biostrings.html",
      "topic": "bioinformatics",
      "tags": [
        "msa",
        "alignment",
        "clustalw",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Aligning multiple sequences via ClustalW, Muscle, or T-Coffee from R"
    },
    {
      "id": "bioinformatics/multiomics-integration",
      "title": "Multi-Omics Integration",
      "url": "tutorials/bioinformatics/multiomics-integration.html",
      "topic": "bioinformatics",
      "tags": [
        "multiomics",
        "MOFA",
        "integration",
        "topic:bioinformatics",
        "omics-and-genomics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Joint analysis of transcriptomic, genomic, and epigenomic layers"
    },
    {
      "id": "bioinformatics/pathway-enrichment-kegg",
      "title": "KEGG Pathway Enrichment",
      "url": "tutorials/bioinformatics/pathway-enrichment-kegg.html",
      "topic": "bioinformatics",
      "tags": [
        "KEGG",
        "enrichment",
        "pathway",
        "topic:bioinformatics",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Over-representation and visualisation against KEGG metabolic/signalling pathways"
    },
    {
      "id": "bioinformatics/pca-of-rnaseq",
      "title": "PCA of RNA-seq Samples",
      "url": "tutorials/bioinformatics/pca-of-rnaseq.html",
      "topic": "bioinformatics",
      "tags": [
        "pca",
        "rna-seq",
        "qc",
        "batch",
        "topic:bioinformatics",
        "multivariate-analysis",
        "omics-and-genomics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Using PCA on variance-stabilised counts to check sample structure and batches"
    },
    {
      "id": "bioinformatics/phylogenetic-trees-ape",
      "title": "Phylogenetic Trees with ape",
      "url": "tutorials/bioinformatics/phylogenetic-trees-ape.html",
      "topic": "bioinformatics",
      "tags": [
        "phylogeny",
        "ape",
        "tree",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Building and interpreting phylogenetic trees using distance-based and maximum-likelihood methods"
    },
    {
      "id": "bioinformatics/population-genetics-basics",
      "title": "Population Genetics Basics",
      "url": "tutorials/bioinformatics/population-genetics-basics.html",
      "topic": "bioinformatics",
      "tags": [
        "population-genetics",
        "hwe",
        "fst",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Allele frequencies, Hardy-Weinberg equilibrium, and F-statistics"
    },
    {
      "id": "bioinformatics/protein-structure-prediction",
      "title": "Protein Structure Prediction",
      "url": "tutorials/bioinformatics/protein-structure-prediction.html",
      "topic": "bioinformatics",
      "tags": [
        "protein",
        "alphafold",
        "structure",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "From sequence to 3-D structure with AlphaFold and RoseTTAFold"
    },
    {
      "id": "bioinformatics/proteomics-msstats",
      "title": "Proteomics with MSstats",
      "url": "tutorials/bioinformatics/proteomics-msstats.html",
      "topic": "bioinformatics",
      "tags": [
        "proteomics",
        "MSstats",
        "LFQ",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Differential protein abundance from label-free or labelled mass-spectrometry data"
    },
    {
      "id": "bioinformatics/read-trimming-adapters",
      "title": "Read Trimming and Adapter Removal",
      "url": "tutorials/bioinformatics/read-trimming-adapters.html",
      "topic": "bioinformatics",
      "tags": [
        "trimming",
        "adapter",
        "rfastp",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Removing low-quality bases and sequencing adapters before alignment"
    },
    {
      "id": "bioinformatics/rnaseq-normalization",
      "title": "RNA-seq Normalisation",
      "url": "tutorials/bioinformatics/rnaseq-normalization.html",
      "topic": "bioinformatics",
      "tags": [
        "normalization",
        "tmm",
        "rle",
        "cpm",
        "topic:bioinformatics",
        "omics-and-genomics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "TMM, RLE, upper-quartile, and CPM: making samples comparable"
    },
    {
      "id": "bioinformatics/salmon-kallisto-pseudoalignment",
      "title": "Pseudoalignment with Salmon and Kallisto",
      "url": "tutorials/bioinformatics/salmon-kallisto-pseudoalignment.html",
      "topic": "bioinformatics",
      "tags": [
        "salmon",
        "kallisto",
        "pseudoalignment",
        "transcript-quantification",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Fast transcript quantification without full alignment"
    },
    {
      "id": "bioinformatics/scrna-cell-type-annotation",
      "title": "Cell-Type Annotation",
      "url": "tutorials/bioinformatics/scrna-cell-type-annotation.html",
      "topic": "bioinformatics",
      "tags": [
        "scrnaseq",
        "annotation",
        "SingleR",
        "topic:bioinformatics",
        "omics-and-genomics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Assigning cell types to clusters using reference-based and manual methods"
    },
    {
      "id": "bioinformatics/scrna-clustering",
      "title": "scRNA Clustering",
      "url": "tutorials/bioinformatics/scrna-clustering.html",
      "topic": "bioinformatics",
      "tags": [
        "scrnaseq",
        "clustering",
        "louvain",
        "leiden",
        "topic:bioinformatics",
        "omics-and-genomics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Graph-based clustering of cells via Louvain or Leiden community detection"
    },
    {
      "id": "bioinformatics/scrna-integration-harmony",
      "title": "Integration with Harmony",
      "url": "tutorials/bioinformatics/scrna-integration-harmony.html",
      "topic": "bioinformatics",
      "tags": [
        "scrnaseq",
        "integration",
        "harmony",
        "topic:bioinformatics",
        "omics-and-genomics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Removing batch effects in scRNA-seq while preserving biological variation"
    },
    {
      "id": "bioinformatics/scrna-marker-genes",
      "title": "Finding Marker Genes",
      "url": "tutorials/bioinformatics/scrna-marker-genes.html",
      "topic": "bioinformatics",
      "tags": [
        "scrnaseq",
        "markers",
        "DE",
        "topic:bioinformatics",
        "multivariate-analysis",
        "omics-and-genomics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Identifying cluster-specific genes for cell-type annotation"
    },
    {
      "id": "bioinformatics/scrna-normalization",
      "title": "scRNA Normalisation",
      "url": "tutorials/bioinformatics/scrna-normalization.html",
      "topic": "bioinformatics",
      "tags": [
        "scrnaseq",
        "normalization",
        "sctransform",
        "topic:bioinformatics",
        "omics-and-genomics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Library-size normalisation and variance stabilisation for scRNA-seq"
    },
    {
      "id": "bioinformatics/scrna-qc-filtering",
      "title": "scRNA QC and Filtering",
      "url": "tutorials/bioinformatics/scrna-qc-filtering.html",
      "topic": "bioinformatics",
      "tags": [
        "scrnaseq",
        "qc",
        "filtering",
        "topic:bioinformatics",
        "omics-and-genomics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Removing low-quality cells before downstream scRNA-seq analysis"
    },
    {
      "id": "bioinformatics/scrna-trajectory-analysis",
      "title": "Trajectory Analysis",
      "url": "tutorials/bioinformatics/scrna-trajectory-analysis.html",
      "topic": "bioinformatics",
      "tags": [
        "scrnaseq",
        "trajectory",
        "pseudotime",
        "topic:bioinformatics",
        "omics-and-genomics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Inferring developmental and activation trajectories from scRNA-seq"
    },
    {
      "id": "bioinformatics/sequence-alignment-overview",
      "title": "Sequence Alignment: Overview",
      "url": "tutorials/bioinformatics/sequence-alignment-overview.html",
      "topic": "bioinformatics",
      "tags": [
        "alignment",
        "needleman-wunsch",
        "smith-waterman",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Global vs local alignment, scoring matrices, and the core algorithms"
    },
    {
      "id": "bioinformatics/single-cell-seurat-intro",
      "title": "Single-Cell with Seurat",
      "url": "tutorials/bioinformatics/single-cell-seurat-intro.html",
      "topic": "bioinformatics",
      "tags": [
        "scrnaseq",
        "seurat",
        "workflow",
        "topic:bioinformatics",
        "omics-and-genomics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "End-to-end workflow for single-cell RNA-seq analysis in Seurat"
    },
    {
      "id": "bioinformatics/spatial-transcriptomics",
      "title": "Spatial Transcriptomics",
      "url": "tutorials/bioinformatics/spatial-transcriptomics.html",
      "topic": "bioinformatics",
      "tags": [
        "spatial",
        "visium",
        "transcriptomics",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Spatially resolved expression analysis with Visium and related platforms"
    },
    {
      "id": "bioinformatics/star-spliced-alignment",
      "title": "STAR: Spliced Alignment",
      "url": "tutorials/bioinformatics/star-spliced-alignment.html",
      "topic": "bioinformatics",
      "tags": [
        "star",
        "spliced-alignment",
        "rna-seq",
        "topic:bioinformatics",
        "omics-and-genomics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Fast splice-aware RNA-seq aligner capable of detecting novel junctions"
    },
    {
      "id": "bioinformatics/structural-variant-detection",
      "title": "Structural Variant Detection",
      "url": "tutorials/bioinformatics/structural-variant-detection.html",
      "topic": "bioinformatics",
      "tags": [
        "SV",
        "manta",
        "delly",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Detecting deletions, duplications, inversions, and translocations from WGS"
    },
    {
      "id": "bioinformatics/surrogate-variable-analysis-sva",
      "title": "Surrogate Variable Analysis (SVA)",
      "url": "tutorials/bioinformatics/surrogate-variable-analysis-sva.html",
      "topic": "bioinformatics",
      "tags": [
        "sva",
        "hidden-confounders",
        "batch",
        "topic:bioinformatics",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Detecting and adjusting for unknown sources of heterogeneity in expression data"
    },
    {
      "id": "bioinformatics/tx-to-gene-summarization",
      "title": "Transcript-to-Gene Summarisation",
      "url": "tutorials/bioinformatics/tx-to-gene-summarization.html",
      "topic": "bioinformatics",
      "tags": [
        "tximport",
        "transcript",
        "gene-level",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Aggregating transcript-level estimates to gene level for standard differential expression"
    },
    {
      "id": "bioinformatics/variant-calling-gatk",
      "title": "Variant Calling with GATK",
      "url": "tutorials/bioinformatics/variant-calling-gatk.html",
      "topic": "bioinformatics",
      "tags": [
        "gatk",
        "variants",
        "HaplotypeCaller",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Short-variant detection from aligned BAMs via the GATK Best Practices pipeline"
    },
    {
      "id": "bioinformatics/vcf-manipulation",
      "title": "VCF Manipulation",
      "url": "tutorials/bioinformatics/vcf-manipulation.html",
      "topic": "bioinformatics",
      "tags": [
        "vcf",
        "filtering",
        "variants",
        "topic:bioinformatics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Reading, filtering, and subsetting VCF files in R and on the command line"
    },
    {
      "id": "bioinformatics/volcano-plots",
      "title": "Volcano Plots",
      "url": "tutorials/bioinformatics/volcano-plots.html",
      "topic": "bioinformatics",
      "tags": [
        "visualisation",
        "volcano",
        "DE",
        "topic:bioinformatics",
        "hypothesis-testing",
        "effect-size"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Visualising significance versus effect size across genes"
    },
    {
      "id": "clinical-biostatistics/adaptive-design",
      "title": "Adaptive Trial Designs",
      "url": "tutorials/clinical-biostatistics/adaptive-design.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "adaptive",
        "design",
        "re-estimation",
        "topic:clinical-biostatistics",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Pre-specified modifications to trial conduct based on interim data"
    },
    {
      "id": "clinical-biostatistics/alpha-spending",
      "title": "Alpha-Spending Functions",
      "url": "tutorials/clinical-biostatistics/alpha-spending.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "alpha-spending",
        "lan-demets",
        "group-sequential",
        "topic:clinical-biostatistics",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Flexible interim alpha allocation via Lan-DeMets spending functions"
    },
    {
      "id": "clinical-biostatistics/baseline-adjustment-ancova",
      "title": "Baseline Adjustment with ANCOVA",
      "url": "tutorials/clinical-biostatistics/baseline-adjustment-ancova.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "ancova",
        "baseline",
        "regression-to-mean",
        "topic:clinical-biostatistics",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Adjusting for baseline covariates to improve precision and address regression to the mean"
    },
    {
      "id": "clinical-biostatistics/bland-altman-analysis",
      "title": "Bland-Altman Limits of Agreement",
      "url": "tutorials/clinical-biostatistics/bland-altman-analysis.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "bland-altman",
        "agreement",
        "method-comparison",
        "topic:clinical-biostatistics",
        "agreement-and-reliability"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Graphical comparison of two measurement methods on the same subjects"
    },
    {
      "id": "clinical-biostatistics/blinding-procedures",
      "title": "Blinding Procedures",
      "url": "tutorials/clinical-biostatistics/blinding-procedures.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "blinding",
        "masking",
        "bias",
        "topic:clinical-biostatistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Masking participants, investigators, assessors, and analysts"
    },
    {
      "id": "clinical-biostatistics/block-randomization",
      "title": "Block Randomisation",
      "url": "tutorials/clinical-biostatistics/block-randomization.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "block",
        "randomisation",
        "balance",
        "topic:clinical-biostatistics",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Random permutations within fixed blocks for balanced allocation"
    },
    {
      "id": "clinical-biostatistics/cluster-rct",
      "title": "Cluster-Randomised Trials",
      "url": "tutorials/clinical-biostatistics/cluster-rct.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "cluster-rct",
        "icc",
        "design-effect",
        "topic:clinical-biostatistics",
        "multivariate-analysis",
        "experimental-design",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Randomisation of clusters (clinics, schools) rather than individuals"
    },
    {
      "id": "clinical-biostatistics/cohens-kappa",
      "title": "Cohen's Kappa",
      "url": "tutorials/clinical-biostatistics/cohens-kappa.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "kappa",
        "agreement",
        "reliability",
        "topic:clinical-biostatistics",
        "effect-size",
        "categorical-data",
        "agreement-and-reliability"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Chance-corrected agreement between two raters on categorical data"
    },
    {
      "id": "clinical-biostatistics/conditional-power",
      "title": "Conditional Power",
      "url": "tutorials/clinical-biostatistics/conditional-power.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "conditional-power",
        "futility",
        "interim",
        "topic:clinical-biostatistics",
        "power-and-sample-size",
        "trial-design",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Probability of eventual trial success given interim data"
    },
    {
      "id": "clinical-biostatistics/cutpoint-selection",
      "title": "Cutpoint Selection",
      "url": "tutorials/clinical-biostatistics/cutpoint-selection.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "cutpoint",
        "threshold",
        "youden",
        "topic:clinical-biostatistics",
        "diagnostic-accuracy"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Youden's J, cost-based, and closest-to-(0,1) criteria for diagnostic thresholds"
    },
    {
      "id": "clinical-biostatistics/diagnostic-accuracy",
      "title": "Diagnostic Test Accuracy",
      "url": "tutorials/clinical-biostatistics/diagnostic-accuracy.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "diagnostic-accuracy",
        "sensitivity",
        "specificity",
        "roc",
        "predictive-values",
        "topic:clinical-biostatistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sensitivity, specificity, predictive values, likelihood ratios, and ROC analysis for binary diagnostic tests"
    },
    {
      "id": "clinical-biostatistics/equivalence-clinical",
      "title": "Clinical Equivalence Trials",
      "url": "tutorials/clinical-biostatistics/equivalence-clinical.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "equivalence",
        "tost",
        "bioequivalence",
        "topic:clinical-biostatistics",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Two one-sided tests (TOST) for bioequivalence and clinical equivalence"
    },
    {
      "id": "clinical-biostatistics/factorial-trial",
      "title": "Factorial Trials",
      "url": "tutorials/clinical-biostatistics/factorial-trial.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "factorial",
        "interaction",
        "design",
        "topic:clinical-biostatistics",
        "experimental-design",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Simultaneously evaluating multiple interventions via a factorial design"
    },
    {
      "id": "clinical-biostatistics/forest-plot-subgroup",
      "title": "Subgroup Forest Plots",
      "url": "tutorials/clinical-biostatistics/forest-plot-subgroup.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "forest-plot",
        "subgroup",
        "visualisation",
        "topic:clinical-biostatistics",
        "exploratory-and-descriptive",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Visual summary of subgroup-specific effects and interaction tests"
    },
    {
      "id": "clinical-biostatistics/icc-continuous-agreement",
      "title": "Intraclass Correlation Coefficient (ICC)",
      "url": "tutorials/clinical-biostatistics/icc-continuous-agreement.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "icc",
        "reliability",
        "agreement",
        "topic:clinical-biostatistics",
        "agreement-and-reliability"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Absolute agreement and consistency for continuous inter-rater reliability"
    },
    {
      "id": "clinical-biostatistics/interim-analysis-group-sequential",
      "title": "Interim Analyses and Group Sequential Designs",
      "url": "tutorials/clinical-biostatistics/interim-analysis-group-sequential.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "interim",
        "group-sequential",
        "alpha-spending",
        "topic:clinical-biostatistics",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Pre-planned interim looks with alpha-spending and early-stopping boundaries"
    },
    {
      "id": "clinical-biostatistics/itt-vs-pp-analysis",
      "title": "ITT vs Per-Protocol Analysis",
      "url": "tutorials/clinical-biostatistics/itt-vs-pp-analysis.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "itt",
        "per-protocol",
        "analysis",
        "topic:clinical-biostatistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Primary analyses under the intent-to-treat and per-protocol principles"
    },
    {
      "id": "clinical-biostatistics/likelihood-ratios",
      "title": "Likelihood Ratios",
      "url": "tutorials/clinical-biostatistics/likelihood-ratios.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "likelihood-ratio",
        "diagnostic",
        "fagan",
        "topic:clinical-biostatistics",
        "diagnostic-accuracy"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "LR+ and LR- as prevalence-independent summaries of diagnostic performance"
    },
    {
      "id": "clinical-biostatistics/minimization-algorithm",
      "title": "Minimisation Algorithm",
      "url": "tutorials/clinical-biostatistics/minimization-algorithm.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "minimisation",
        "adaptive",
        "randomisation",
        "topic:clinical-biostatistics",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Covariate-adaptive allocation that prospectively minimises imbalance"
    },
    {
      "id": "clinical-biostatistics/missing-data-rct",
      "title": "Missing Data in RCTs",
      "url": "tutorials/clinical-biostatistics/missing-data-rct.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "missing-data",
        "mcar",
        "mar",
        "mnar",
        "topic:clinical-biostatistics",
        "experimental-design",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "MCAR, MAR, MNAR and their implications for primary analysis"
    },
    {
      "id": "clinical-biostatistics/multiple-imputation",
      "title": "Multiple Imputation",
      "url": "tutorials/clinical-biostatistics/multiple-imputation.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "multiple-imputation",
        "mice",
        "rubin",
        "topic:clinical-biostatistics",
        "missing-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Imputing multiple plausible values and combining via Rubin's rules"
    },
    {
      "id": "clinical-biostatistics/non-inferiority-margin",
      "title": "Non-Inferiority Margin Selection",
      "url": "tutorials/clinical-biostatistics/non-inferiority-margin.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "non-inferiority",
        "margin",
        "synthesis",
        "topic:clinical-biostatistics",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Defining the clinically acceptable maximum inferiority"
    },
    {
      "id": "clinical-biostatistics/obrien-fleming-boundary",
      "title": "O'Brien-Fleming Boundary",
      "url": "tutorials/clinical-biostatistics/obrien-fleming-boundary.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "obrien-fleming",
        "boundary",
        "group-sequential",
        "topic:clinical-biostatistics",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Conservative early stopping boundary in group-sequential trials"
    },
    {
      "id": "clinical-biostatistics/pocock-boundary",
      "title": "Pocock Boundary",
      "url": "tutorials/clinical-biostatistics/pocock-boundary.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "pocock",
        "boundary",
        "group-sequential",
        "topic:clinical-biostatistics",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Constant nominal alpha across interim analyses"
    },
    {
      "id": "clinical-biostatistics/predictive-values-prevalence",
      "title": "Predictive Values and Prevalence",
      "url": "tutorials/clinical-biostatistics/predictive-values-prevalence.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "ppv",
        "npv",
        "prevalence",
        "bayes",
        "topic:clinical-biostatistics",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "How disease prevalence determines PPV and NPV via Bayes' theorem"
    },
    {
      "id": "clinical-biostatistics/randomization-methods",
      "title": "Randomisation Methods",
      "url": "tutorials/clinical-biostatistics/randomization-methods.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "randomisation",
        "allocation",
        "trials",
        "topic:clinical-biostatistics",
        "experimental-design",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Simple, block, and stratified randomisation for RCT allocation"
    },
    {
      "id": "clinical-biostatistics/rct-design-crossover",
      "title": "Crossover RCT Design",
      "url": "tutorials/clinical-biostatistics/rct-design-crossover.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "crossover",
        "washout",
        "carryover",
        "topic:clinical-biostatistics",
        "experimental-design",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Within-subject comparison across treatment periods with washout"
    },
    {
      "id": "clinical-biostatistics/rct-design-parallel",
      "title": "Parallel-Group RCT Design",
      "url": "tutorials/clinical-biostatistics/rct-design-parallel.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "rct",
        "parallel-group",
        "design",
        "topic:clinical-biostatistics",
        "experimental-design",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The two-arm randomised controlled trial: structure, analysis, and reporting"
    },
    {
      "id": "clinical-biostatistics/reliability-cronbach-alpha",
      "title": "Reliability and Cronbach's Alpha",
      "url": "tutorials/clinical-biostatistics/reliability-cronbach-alpha.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "cronbach",
        "reliability",
        "internal-consistency",
        "topic:clinical-biostatistics",
        "agreement-and-reliability"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Internal consistency of multi-item scales"
    },
    {
      "id": "clinical-biostatistics/roc-analysis",
      "title": "ROC Analysis",
      "url": "tutorials/clinical-biostatistics/roc-analysis.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "roc",
        "auc",
        "diagnostic",
        "topic:clinical-biostatistics",
        "diagnostic-accuracy"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Receiver Operating Characteristic curves and area under the curve"
    },
    {
      "id": "clinical-biostatistics/sample-size-reestimation",
      "title": "Sample Size Re-Estimation",
      "url": "tutorials/clinical-biostatistics/sample-size-reestimation.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "ssr",
        "adaptive",
        "nuisance",
        "topic:clinical-biostatistics",
        "power-and-sample-size",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Updating trial sample size mid-study using blinded or unblinded nuisance parameter estimates"
    },
    {
      "id": "clinical-biostatistics/sensitivity-analysis-clinical",
      "title": "Sensitivity Analyses in Clinical Trials",
      "url": "tutorials/clinical-biostatistics/sensitivity-analysis-clinical.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "sensitivity",
        "tipping-point",
        "mnar",
        "topic:clinical-biostatistics",
        "power-and-sample-size",
        "diagnostic-accuracy",
        "missing-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Exploring robustness of conclusions to assumptions about missing data and model choice"
    },
    {
      "id": "clinical-biostatistics/stepped-wedge-trial",
      "title": "Stepped-Wedge Trial",
      "url": "tutorials/clinical-biostatistics/stepped-wedge-trial.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "stepped-wedge",
        "rollout",
        "cluster",
        "topic:clinical-biostatistics",
        "multivariate-analysis",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sequential rollout cluster design with all clusters eventually treated"
    },
    {
      "id": "clinical-biostatistics/stratified-randomization",
      "title": "Stratified Randomisation",
      "url": "tutorials/clinical-biostatistics/stratified-randomization.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "stratification",
        "randomisation",
        "balance",
        "topic:clinical-biostatistics",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Separate randomisation lists within levels of baseline covariates"
    },
    {
      "id": "clinical-biostatistics/subgroup-analysis",
      "title": "Subgroup Analyses",
      "url": "tutorials/clinical-biostatistics/subgroup-analysis.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "subgroup",
        "interaction",
        "heterogeneity",
        "topic:clinical-biostatistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Pre-specified subgroup effects and treatment-by-subgroup interaction tests"
    },
    {
      "id": "clinical-biostatistics/weighted-kappa",
      "title": "Weighted Kappa",
      "url": "tutorials/clinical-biostatistics/weighted-kappa.html",
      "topic": "clinical-biostatistics",
      "tags": [
        "weighted-kappa",
        "ordinal",
        "agreement",
        "topic:clinical-biostatistics",
        "agreement-and-reliability"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Ordinal inter-rater agreement with linear or quadratic weights"
    },
    {
      "id": "descriptive-statistics/coefficient-of-variation",
      "title": "Coefficient of Variation",
      "url": "tutorials/descriptive-statistics/coefficient-of-variation.html",
      "topic": "descriptive-statistics",
      "tags": [
        "coefficient-of-variation",
        "cv",
        "relative-dispersion",
        "topic:descriptive-statistics",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Scale-free dispersion: the ratio of the standard deviation to the mean"
    },
    {
      "id": "descriptive-statistics/contingency-tables",
      "title": "Contingency Tables",
      "url": "tutorials/descriptive-statistics/contingency-tables.html",
      "topic": "descriptive-statistics",
      "tags": [
        "contingency-table",
        "expected-counts",
        "residuals",
        "topic:descriptive-statistics",
        "categorical-data",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "r × c tables of counts, their expected values under independence, and standardised residuals"
    },
    {
      "id": "descriptive-statistics/cross-tabulations",
      "title": "Cross-Tabulations",
      "url": "tutorials/descriptive-statistics/cross-tabulations.html",
      "topic": "descriptive-statistics",
      "tags": [
        "cross-tab",
        "contingency-table",
        "joint-distribution",
        "topic:descriptive-statistics",
        "categorical-data",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Two-way and higher-dimensional tables for joint distributions of categorical variables"
    },
    {
      "id": "descriptive-statistics/descriptive-vs-inferential",
      "title": "Descriptive vs Inferential Statistics",
      "url": "tutorials/descriptive-statistics/descriptive-vs-inferential.html",
      "topic": "descriptive-statistics",
      "tags": [
        "descriptive",
        "inferential",
        "sample",
        "population",
        "topic:descriptive-statistics",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The distinction between summarising the sample and making claims about the population"
    },
    {
      "id": "descriptive-statistics/five-number-summary",
      "title": "Five-Number Summary",
      "url": "tutorials/descriptive-statistics/five-number-summary.html",
      "topic": "descriptive-statistics",
      "tags": [
        "five-number-summary",
        "boxplot",
        "tukey",
        "quartile",
        "topic:descriptive-statistics",
        "exploratory-and-descriptive",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Min, Q1, median, Q3, max: the compact distribution summary that powers the boxplot"
    },
    {
      "id": "descriptive-statistics/frequency-tables",
      "title": "Frequency Tables",
      "url": "tutorials/descriptive-statistics/frequency-tables.html",
      "topic": "descriptive-statistics",
      "tags": [
        "frequency-table",
        "counts",
        "proportions",
        "topic:descriptive-statistics",
        "categorical-data",
        "exploratory-and-descriptive",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Counting categorical and discrete-numeric variables: absolute, relative, and cumulative frequencies"
    },
    {
      "id": "descriptive-statistics/geometric-mean",
      "title": "The Geometric Mean",
      "url": "tutorials/descriptive-statistics/geometric-mean.html",
      "topic": "descriptive-statistics",
      "tags": [
        "geometric-mean",
        "log-transformation",
        "multiplicative",
        "topic:descriptive-statistics",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Log-scale averaging for multiplicative data: ratios, growth rates, titres"
    },
    {
      "id": "descriptive-statistics/harmonic-mean",
      "title": "The Harmonic Mean",
      "url": "tutorials/descriptive-statistics/harmonic-mean.html",
      "topic": "descriptive-statistics",
      "tags": [
        "harmonic-mean",
        "rates",
        "f1-score",
        "topic:descriptive-statistics",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Reciprocal-averaged summary for rates, speeds, and F1-style composite scores"
    },
    {
      "id": "descriptive-statistics/interquartile-range",
      "title": "The Interquartile Range",
      "url": "tutorials/descriptive-statistics/interquartile-range.html",
      "topic": "descriptive-statistics",
      "tags": [
        "iqr",
        "quartile",
        "boxplot",
        "robust",
        "topic:descriptive-statistics",
        "robust-statistics",
        "exploratory-and-descriptive",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Width of the central 50% of the distribution: robust, interpretable, and the basis of Tukey fences"
    },
    {
      "id": "descriptive-statistics/kurtosis",
      "title": "Kurtosis",
      "url": "tutorials/descriptive-statistics/kurtosis.html",
      "topic": "descriptive-statistics",
      "tags": [
        "kurtosis",
        "tail-heaviness",
        "excess-kurtosis",
        "topic:descriptive-statistics",
        "exploratory-and-descriptive",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Quantifying tail heaviness of a distribution, and interpreting excess kurtosis"
    },
    {
      "id": "descriptive-statistics/mean-absolute-deviation",
      "title": "Mean Absolute Deviation",
      "url": "tutorials/descriptive-statistics/mean-absolute-deviation.html",
      "topic": "descriptive-statistics",
      "tags": [
        "mad",
        "mean-absolute-deviation",
        "robust",
        "topic:descriptive-statistics",
        "robust-statistics",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Average absolute deviation from the mean or median: a robust alternative to the standard deviation"
    },
    {
      "id": "descriptive-statistics/measures-of-dispersion",
      "title": "Measures of Dispersion",
      "url": "tutorials/descriptive-statistics/measures-of-dispersion.html",
      "topic": "descriptive-statistics",
      "tags": [
        "dispersion",
        "variance",
        "sd",
        "iqr",
        "mad",
        "topic:descriptive-statistics",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Quantifying how spread out the data are: variance, SD, MAD, IQR, and range"
    },
    {
      "id": "descriptive-statistics/measures-of-location",
      "title": "Measures of Central Tendency",
      "url": "tutorials/descriptive-statistics/measures-of-location.html",
      "topic": "descriptive-statistics",
      "tags": [
        "mean",
        "median",
        "mode",
        "trimmed-mean",
        "robust-statistics",
        "topic:descriptive-statistics",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Mean, median, mode, and their robust cousins -- when each is appropriate and how to compute them in R"
    },
    {
      "id": "descriptive-statistics/measures-of-shape",
      "title": "Measures of Shape",
      "url": "tutorials/descriptive-statistics/measures-of-shape.html",
      "topic": "descriptive-statistics",
      "tags": [
        "skewness",
        "kurtosis",
        "shape",
        "distribution",
        "topic:descriptive-statistics",
        "exploratory-and-descriptive",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Skewness and kurtosis: summarising the asymmetry and tail heaviness of a distribution beyond location and spread"
    },
    {
      "id": "descriptive-statistics/outlier-detection-rules",
      "title": "Outlier Detection Rules",
      "url": "tutorials/descriptive-statistics/outlier-detection-rules.html",
      "topic": "descriptive-statistics",
      "tags": [
        "outlier",
        "iqr-fence",
        "z-score",
        "hampel",
        "grubbs",
        "topic:descriptive-statistics",
        "robust-statistics",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Univariate rules: 1.5·IQR fences, z-score thresholds, Hampel identifier, and Grubbs' test"
    },
    {
      "id": "descriptive-statistics/percentile-rank",
      "title": "Percentile Ranks",
      "url": "tutorials/descriptive-statistics/percentile-rank.html",
      "topic": "descriptive-statistics",
      "tags": [
        "percentile-rank",
        "rank",
        "ecdf",
        "topic:descriptive-statistics",
        "exploratory-and-descriptive",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Converting a raw value to its position in the sample distribution, expressed as a percentage"
    },
    {
      "id": "descriptive-statistics/quantiles-and-percentiles",
      "title": "Quantiles and Percentiles",
      "url": "tutorials/descriptive-statistics/quantiles-and-percentiles.html",
      "topic": "descriptive-statistics",
      "tags": [
        "quantile",
        "percentile",
        "median",
        "quartile",
        "topic:descriptive-statistics",
        "exploratory-and-descriptive",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Dividing a distribution by cumulative probability: the median, quartiles, percentiles, and R's nine quantile definitions"
    },
    {
      "id": "descriptive-statistics/robust-statistics-overview",
      "title": "Robust Statistics: An Overview",
      "url": "tutorials/descriptive-statistics/robust-statistics-overview.html",
      "topic": "descriptive-statistics",
      "tags": [
        "robust",
        "breakdown-point",
        "influence-function",
        "topic:descriptive-statistics",
        "robust-statistics",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Estimators designed to remain close to the truth under contamination, with key concepts: breakdown point, influence function, efficiency"
    },
    {
      "id": "descriptive-statistics/skewness",
      "title": "Skewness",
      "url": "tutorials/descriptive-statistics/skewness.html",
      "topic": "descriptive-statistics",
      "tags": [
        "skewness",
        "shape",
        "asymmetry",
        "topic:descriptive-statistics",
        "exploratory-and-descriptive",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Measuring the asymmetry of a distribution: sample estimators, interpretation, and typical values in applied data"
    },
    {
      "id": "descriptive-statistics/standardisation-zscore",
      "title": "Standardisation and z-Scores",
      "url": "tutorials/descriptive-statistics/standardisation-zscore.html",
      "topic": "descriptive-statistics",
      "tags": [
        "z-score",
        "standardisation",
        "scale",
        "robust-z",
        "topic:descriptive-statistics",
        "robust-statistics",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Centring and scaling a variable, and the robust-z alternative"
    },
    {
      "id": "descriptive-statistics/stem-and-leaf-plots",
      "title": "Stem-and-Leaf Plots",
      "url": "tutorials/descriptive-statistics/stem-and-leaf-plots.html",
      "topic": "descriptive-statistics",
      "tags": [
        "stem-and-leaf",
        "tukey",
        "exploratory",
        "topic:descriptive-statistics",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "A text-based compact display of small samples that preserves every data value"
    },
    {
      "id": "descriptive-statistics/summary-by-group",
      "title": "Summary by Group",
      "url": "tutorials/descriptive-statistics/summary-by-group.html",
      "topic": "descriptive-statistics",
      "tags": [
        "dplyr",
        "group-by",
        "summarise",
        "split-apply-combine",
        "topic:descriptive-statistics",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Split-apply-combine pattern for computing summaries within subgroups of the data"
    },
    {
      "id": "descriptive-statistics/trimmed-winsorized-mean",
      "title": "Trimmed and Winsorised Means",
      "url": "tutorials/descriptive-statistics/trimmed-winsorized-mean.html",
      "topic": "descriptive-statistics",
      "tags": [
        "trimmed-mean",
        "winsorized-mean",
        "robust",
        "location",
        "topic:descriptive-statistics",
        "robust-statistics",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Robust location estimators that compromise between the mean and the median"
    },
    {
      "id": "descriptive-statistics/variance-and-sd",
      "title": "Variance and Standard Deviation",
      "url": "tutorials/descriptive-statistics/variance-and-sd.html",
      "topic": "descriptive-statistics",
      "tags": [
        "variance",
        "sd",
        "bessel-correction",
        "bias",
        "topic:descriptive-statistics",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Squared and unsquared dispersion, the n vs n-1 divisor, and why the sample SD is biased"
    },
    {
      "id": "descriptive-statistics/weighted-mean",
      "title": "The Weighted Mean",
      "url": "tutorials/descriptive-statistics/weighted-mean.html",
      "topic": "descriptive-statistics",
      "tags": [
        "weighted-mean",
        "survey",
        "pooling",
        "topic:descriptive-statistics",
        "exploratory-and-descriptive",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Averaging with unequal weights: survey inclusion probabilities, meta-analysis pooling, and more"
    },
    {
      "id": "experimental-design/balanced-incomplete-block",
      "title": "Balanced Incomplete Block Designs",
      "url": "tutorials/experimental-design/balanced-incomplete-block.html",
      "topic": "experimental-design",
      "tags": [
        "bibd",
        "blocks",
        "incomplete",
        "topic:experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "BIBDs when block size is smaller than treatment count"
    },
    {
      "id": "experimental-design/blocking-principles",
      "title": "Principles of Blocking",
      "url": "tutorials/experimental-design/blocking-principles.html",
      "topic": "experimental-design",
      "tags": [
        "blocking",
        "nuisance",
        "precision",
        "topic:experimental-design",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Local control of nuisance variability for precision gains"
    },
    {
      "id": "experimental-design/box-behnken-design",
      "title": "Box-Behnken Designs",
      "url": "tutorials/experimental-design/box-behnken-design.html",
      "topic": "experimental-design",
      "tags": [
        "box-behnken",
        "rsm",
        "three-level",
        "topic:experimental-design",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Three-level rotatable designs for RSM without factorial-corner runs"
    },
    {
      "id": "experimental-design/central-composite-design",
      "title": "Central Composite Designs",
      "url": "tutorials/experimental-design/central-composite-design.html",
      "topic": "experimental-design",
      "tags": [
        "ccd",
        "rsm",
        "axial",
        "topic:experimental-design",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Response-surface designs combining factorial, axial, and centre points"
    },
    {
      "id": "experimental-design/completely-randomized-design",
      "title": "Completely Randomised Design",
      "url": "tutorials/experimental-design/completely-randomized-design.html",
      "topic": "experimental-design",
      "tags": [
        "crd",
        "randomisation",
        "anova",
        "topic:experimental-design",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The simplest experimental design: random assignment of treatments to units"
    },
    {
      "id": "experimental-design/constrained-mixture",
      "title": "Constrained Mixture Designs",
      "url": "tutorials/experimental-design/constrained-mixture.html",
      "topic": "experimental-design",
      "tags": [
        "constrained-mixture",
        "extreme-vertices",
        "bounds",
        "topic:experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Mixture experiments with lower and upper bounds on components"
    },
    {
      "id": "experimental-design/crossover-designs",
      "title": "Crossover Designs",
      "url": "tutorials/experimental-design/crossover-designs.html",
      "topic": "experimental-design",
      "tags": [
        "crossover",
        "ab-ba",
        "carryover",
        "topic:experimental-design",
        "experimental-design",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Within-subject comparison of treatments with washout and carryover consideration"
    },
    {
      "id": "experimental-design/desirability-function",
      "title": "Desirability Functions",
      "url": "tutorials/experimental-design/desirability-function.html",
      "topic": "experimental-design",
      "tags": [
        "desirability",
        "multi-response",
        "optimisation",
        "topic:experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Multi-response optimisation via geometric-mean desirability scores"
    },
    {
      "id": "experimental-design/factorial-2k",
      "title": "2^k Factorial Designs",
      "url": "tutorials/experimental-design/factorial-2k.html",
      "topic": "experimental-design",
      "tags": [
        "factorial",
        "2k",
        "interactions",
        "topic:experimental-design",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Full factorial experiments with k two-level factors"
    },
    {
      "id": "experimental-design/factorial-3k",
      "title": "3^k Factorial Designs",
      "url": "tutorials/experimental-design/factorial-3k.html",
      "topic": "experimental-design",
      "tags": [
        "factorial",
        "3k",
        "quadratic",
        "topic:experimental-design",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Full factorial experiments with k three-level factors"
    },
    {
      "id": "experimental-design/fractional-factorial-design",
      "title": "Fractional Factorial Designs",
      "url": "tutorials/experimental-design/fractional-factorial-design.html",
      "topic": "experimental-design",
      "tags": [
        "fractional",
        "screening",
        "confounding",
        "topic:experimental-design",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "A fraction of a 2^k design: economical screening with acceptable confounding"
    },
    {
      "id": "experimental-design/graeco-latin-square",
      "title": "Graeco-Latin Square Designs",
      "url": "tutorials/experimental-design/graeco-latin-square.html",
      "topic": "experimental-design",
      "tags": [
        "graeco-latin",
        "blocking",
        "orthogonal",
        "topic:experimental-design",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Blocking on three nuisance factors by superimposing two orthogonal Latin squares"
    },
    {
      "id": "experimental-design/latin-square",
      "title": "Latin Square Designs",
      "url": "tutorials/experimental-design/latin-square.html",
      "topic": "experimental-design",
      "tags": [
        "latin-square",
        "blocking",
        "design",
        "topic:experimental-design",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Blocking on two nuisance factors via a Latin-square arrangement"
    },
    {
      "id": "experimental-design/mixture-centroid",
      "title": "Simplex Centroid Designs",
      "url": "tutorials/experimental-design/mixture-centroid.html",
      "topic": "experimental-design",
      "tags": [
        "mixture",
        "centroid",
        "scheffe",
        "topic:experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Mixture designs including centroids of all sub-simplices"
    },
    {
      "id": "experimental-design/mixture-designs-simplex",
      "title": "Simplex Lattice Mixture Designs",
      "url": "tutorials/experimental-design/mixture-designs-simplex.html",
      "topic": "experimental-design",
      "tags": [
        "mixture",
        "simplex-lattice",
        "scheffe",
        "topic:experimental-design",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Experimental designs for component-proportion factors that sum to 1"
    },
    {
      "id": "experimental-design/optimal-design-d-a-i",
      "title": "Optimal Designs: D, A, I",
      "url": "tutorials/experimental-design/optimal-design-d-a-i.html",
      "topic": "experimental-design",
      "tags": [
        "optimal-design",
        "d-optimal",
        "criteria",
        "topic:experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "D-, A-, I-, and G-optimality criteria for computer-generated designs"
    },
    {
      "id": "experimental-design/orthogonal-arrays",
      "title": "Orthogonal Arrays",
      "url": "tutorials/experimental-design/orthogonal-arrays.html",
      "topic": "experimental-design",
      "tags": [
        "orthogonal-array",
        "taguchi",
        "fractional",
        "topic:experimental-design",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Tabulated fractional-factorial arrays L4, L8, L9, L16, L27"
    },
    {
      "id": "experimental-design/plackett-burman",
      "title": "Plackett-Burman Designs",
      "url": "tutorials/experimental-design/plackett-burman.html",
      "topic": "experimental-design",
      "tags": [
        "plackett-burman",
        "screening",
        "design",
        "topic:experimental-design",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Highly efficient two-level screening designs for many factors"
    },
    {
      "id": "experimental-design/power-in-doe",
      "title": "Power Analysis for DOE",
      "url": "tutorials/experimental-design/power-in-doe.html",
      "topic": "experimental-design",
      "tags": [
        "power",
        "sample-size",
        "doe",
        "topic:experimental-design",
        "power-and-sample-size",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Calculating power for detecting main effects and interactions in designed experiments"
    },
    {
      "id": "experimental-design/randomised-complete-block",
      "title": "Randomised Complete Block Design",
      "url": "tutorials/experimental-design/randomised-complete-block.html",
      "topic": "experimental-design",
      "tags": [
        "rcbd",
        "blocking",
        "anova",
        "experimental-design",
        "topic:experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Blocking to control known nuisance variation, with construction, analysis, and interpretation in R"
    },
    {
      "id": "experimental-design/randomization-in-design",
      "title": "Randomisation in Design",
      "url": "tutorials/experimental-design/randomization-in-design.html",
      "topic": "experimental-design",
      "tags": [
        "randomisation",
        "doe",
        "protection",
        "topic:experimental-design",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Purpose, procedures, and role of randomisation in experimental design"
    },
    {
      "id": "experimental-design/repeated-measures-design",
      "title": "Repeated-Measures Designs",
      "url": "tutorials/experimental-design/repeated-measures-design.html",
      "topic": "experimental-design",
      "tags": [
        "repeated-measures",
        "within-subject",
        "mixed-effects",
        "topic:experimental-design",
        "longitudinal-and-mixed-models"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Designs with a within-subject factor: multiple measurements per unit"
    },
    {
      "id": "experimental-design/resolution-aliasing",
      "title": "Resolution and Aliasing",
      "url": "tutorials/experimental-design/resolution-aliasing.html",
      "topic": "experimental-design",
      "tags": [
        "resolution",
        "aliasing",
        "confounding",
        "topic:experimental-design",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Understanding the confounding structure of fractional factorial designs"
    },
    {
      "id": "experimental-design/response-surface-methodology",
      "title": "Response Surface Methodology",
      "url": "tutorials/experimental-design/response-surface-methodology.html",
      "topic": "experimental-design",
      "tags": [
        "rsm",
        "optimisation",
        "second-order",
        "topic:experimental-design",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Optimising processes via second-order polynomial response surfaces"
    },
    {
      "id": "experimental-design/robust-parameter-design",
      "title": "Robust Parameter Design",
      "url": "tutorials/experimental-design/robust-parameter-design.html",
      "topic": "experimental-design",
      "tags": [
        "robust-design",
        "taguchi",
        "variance",
        "topic:experimental-design",
        "robust-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Choosing controllable factor levels to minimise response variance across noise factors"
    },
    {
      "id": "experimental-design/signal-to-noise-ratio",
      "title": "Signal-to-Noise Ratios",
      "url": "tutorials/experimental-design/signal-to-noise-ratio.html",
      "topic": "experimental-design",
      "tags": [
        "snr",
        "taguchi",
        "signal",
        "topic:experimental-design",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Taguchi's SNR metrics combining mean and variance"
    },
    {
      "id": "experimental-design/split-plot-design",
      "title": "Split-Plot Designs",
      "url": "tutorials/experimental-design/split-plot-design.html",
      "topic": "experimental-design",
      "tags": [
        "split-plot",
        "mixed-effects",
        "hard-to-change",
        "topic:experimental-design",
        "experimental-design",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Designs with hard-to-change and easy-to-change factors"
    },
    {
      "id": "experimental-design/steepest-ascent",
      "title": "Method of Steepest Ascent",
      "url": "tutorials/experimental-design/steepest-ascent.html",
      "topic": "experimental-design",
      "tags": [
        "steepest-ascent",
        "rsm",
        "optimisation",
        "topic:experimental-design",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Moving along the gradient of a first-order response surface toward the optimum"
    },
    {
      "id": "experimental-design/strip-plot-design",
      "title": "Strip-Plot Designs",
      "url": "tutorials/experimental-design/strip-plot-design.html",
      "topic": "experimental-design",
      "tags": [
        "strip-plot",
        "hard-to-change",
        "mixed-effects",
        "topic:experimental-design",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Designs with two hard-to-change factors applied to perpendicular strips"
    },
    {
      "id": "experimental-design/taguchi-methods",
      "title": "Taguchi Methods",
      "url": "tutorials/experimental-design/taguchi-methods.html",
      "topic": "experimental-design",
      "tags": [
        "taguchi",
        "orthogonal-array",
        "quality",
        "topic:experimental-design",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Orthogonal-array designs and signal-to-noise optimisation for quality engineering"
    },
    {
      "id": "inference/anderson-darling-test",
      "title": "Anderson-Darling Test",
      "url": "tutorials/inference/anderson-darling-test.html",
      "topic": "inference",
      "tags": [
        "anderson-darling",
        "goodness-of-fit",
        "normality",
        "tails",
        "topic:inferential-statistics",
        "hypothesis-testing",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "A distribution goodness-of-fit test with emphasis on the tails"
    },
    {
      "id": "inference/bartlett-test",
      "title": "Bartlett's Test",
      "url": "tutorials/inference/bartlett-test.html",
      "topic": "inference",
      "tags": [
        "bartlett",
        "variance-homogeneity",
        "normality-sensitive",
        "topic:inferential-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Test of equal variances across groups, most powerful under normality but very sensitive to its violation"
    },
    {
      "id": "inference/benjamini-hochberg-fdr",
      "title": "Benjamini-Hochberg FDR",
      "url": "tutorials/inference/benjamini-hochberg-fdr.html",
      "topic": "inference",
      "tags": [
        "fdr",
        "benjamini-hochberg",
        "multiple-testing",
        "discovery",
        "topic:inferential-statistics",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Controlling the expected proportion of false discoveries among rejected hypotheses"
    },
    {
      "id": "inference/benjamini-yekutieli",
      "title": "Benjamini-Yekutieli FDR",
      "url": "tutorials/inference/benjamini-yekutieli.html",
      "topic": "inference",
      "tags": [
        "by",
        "benjamini-yekutieli",
        "fdr",
        "dependence",
        "topic:inferential-statistics",
        "robust-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Dependence-robust false discovery rate control at the cost of conservatism"
    },
    {
      "id": "inference/bonferroni-correction",
      "title": "Bonferroni Correction",
      "url": "tutorials/inference/bonferroni-correction.html",
      "topic": "inference",
      "tags": [
        "bonferroni",
        "multiple-testing",
        "family-wise-error",
        "topic:inferential-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Controlling family-wise error rate by dividing alpha across m tests"
    },
    {
      "id": "inference/bootstrap-confidence-intervals",
      "title": "Bootstrap Confidence Intervals",
      "url": "tutorials/inference/bootstrap-confidence-intervals.html",
      "topic": "inference",
      "tags": [
        "bootstrap",
        "confidence-interval",
        "bca",
        "percentile",
        "topic:inferential-statistics",
        "non-parametric"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Percentile, basic, BCa, and studentised bootstrap CIs"
    },
    {
      "id": "inference/bootstrap-introduction",
      "title": "The Bootstrap: Introduction",
      "url": "tutorials/inference/bootstrap-introduction.html",
      "topic": "inference",
      "tags": [
        "bootstrap",
        "resampling",
        "standard-error",
        "non-parametric",
        "topic:inferential-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Resampling with replacement to estimate standard errors and sampling distributions"
    },
    {
      "id": "inference/chi-squared-goodness-of-fit",
      "title": "Chi-Squared Goodness-of-Fit Test",
      "url": "tutorials/inference/chi-squared-goodness-of-fit.html",
      "topic": "inference",
      "tags": [
        "chi-squared",
        "goodness-of-fit",
        "categorical",
        "topic:inferential-statistics",
        "hypothesis-testing",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Testing whether observed category frequencies match an expected distribution"
    },
    {
      "id": "inference/chi-squared-independence",
      "title": "Chi-Squared Test of Independence",
      "url": "tutorials/inference/chi-squared-independence.html",
      "topic": "inference",
      "tags": [
        "chi-squared",
        "independence",
        "contingency",
        "cramer-v",
        "topic:inferential-statistics",
        "hypothesis-testing",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Testing association between two categorical variables via observed vs. expected counts under independence"
    },
    {
      "id": "inference/cochran-mantel-haenszel",
      "title": "Cochran-Mantel-Haenszel Test",
      "url": "tutorials/inference/cochran-mantel-haenszel.html",
      "topic": "inference",
      "tags": [
        "cmh",
        "stratified",
        "2x2",
        "confounding",
        "topic:inferential-statistics",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Stratified analysis of 2x2 tables across levels of a confounding variable"
    },
    {
      "id": "inference/effect-sizes-overview",
      "title": "Effect Sizes: Overview",
      "url": "tutorials/inference/effect-sizes-overview.html",
      "topic": "inference",
      "tags": [
        "effect-size",
        "cohen-d",
        "eta-squared",
        "reporting",
        "topic:inferential-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Why effect sizes matter and which measures apply to which tests"
    },
    {
      "id": "inference/equivalence-tost",
      "title": "Equivalence Testing with TOST",
      "url": "tutorials/inference/equivalence-tost.html",
      "topic": "inference",
      "tags": [
        "tost",
        "equivalence",
        "non-inferiority",
        "bioequivalence",
        "topic:inferential-statistics",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Two one-sided tests for establishing practical equivalence within a margin"
    },
    {
      "id": "inference/fisher-exact-test",
      "title": "Fisher's Exact Test",
      "url": "tutorials/inference/fisher-exact-test.html",
      "topic": "inference",
      "tags": [
        "fisher-exact",
        "hypergeometric",
        "small-samples",
        "topic:inferential-statistics",
        "hypothesis-testing",
        "categorical-data",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Exact test for 2x2 (and r x c) contingency tables, based on the hypergeometric distribution"
    },
    {
      "id": "inference/friedman-test",
      "title": "Friedman Test",
      "url": "tutorials/inference/friedman-test.html",
      "topic": "inference",
      "tags": [
        "friedman",
        "repeated-measures",
        "ranks",
        "non-parametric",
        "topic:inferential-statistics",
        "hypothesis-testing",
        "longitudinal-and-mixed-models"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Non-parametric test for three or more paired / repeated measures"
    },
    {
      "id": "inference/holm-correction",
      "title": "Holm's Step-Down Correction",
      "url": "tutorials/inference/holm-correction.html",
      "topic": "inference",
      "tags": [
        "holm",
        "sequential",
        "multiple-testing",
        "fwer",
        "topic:inferential-statistics",
        "power-and-sample-size"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sequential Bonferroni with uniformly greater power; controls FWER"
    },
    {
      "id": "inference/jackknife",
      "title": "The Jackknife",
      "url": "tutorials/inference/jackknife.html",
      "topic": "inference",
      "tags": [
        "jackknife",
        "leave-one-out",
        "bias",
        "variance",
        "topic:inferential-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Leave-one-out resampling for bias and variance estimation"
    },
    {
      "id": "inference/kendall-tau",
      "title": "Kendall's Tau",
      "url": "tutorials/inference/kendall-tau.html",
      "topic": "inference",
      "tags": [
        "kendall",
        "tau",
        "concordance",
        "topic:inferential-statistics",
        "non-parametric",
        "robust-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Rank correlation based on concordant vs discordant pairs; robust to ties and small samples"
    },
    {
      "id": "inference/kolmogorov-smirnov-test",
      "title": "Kolmogorov-Smirnov Test",
      "url": "tutorials/inference/kolmogorov-smirnov-test.html",
      "topic": "inference",
      "tags": [
        "kolmogorov-smirnov",
        "goodness-of-fit",
        "ecdf",
        "topic:inferential-statistics",
        "hypothesis-testing",
        "non-parametric"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "CDF-based goodness-of-fit for one-sample and two-sample comparisons"
    },
    {
      "id": "inference/kruskal-wallis",
      "title": "Kruskal-Wallis Test",
      "url": "tutorials/inference/kruskal-wallis.html",
      "topic": "inference",
      "tags": [
        "kruskal-wallis",
        "non-parametric",
        "ranks",
        "dunn",
        "topic:inferential-statistics",
        "hypothesis-testing"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Non-parametric one-way ANOVA based on ranks across three or more groups"
    },
    {
      "id": "inference/levene-test",
      "title": "Levene's Test of Variances",
      "url": "tutorials/inference/levene-test.html",
      "topic": "inference",
      "tags": [
        "levene",
        "variance-homogeneity",
        "robust",
        "topic:inferential-statistics",
        "robust-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Robust test of variance homogeneity across groups"
    },
    {
      "id": "inference/mann-whitney-u",
      "title": "Mann-Whitney U Test",
      "url": "tutorials/inference/mann-whitney-u.html",
      "topic": "inference",
      "tags": [
        "mann-whitney",
        "wilcoxon-rank-sum",
        "non-parametric",
        "ranks",
        "topic:inferential-statistics",
        "hypothesis-testing"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Non-parametric two-sample test based on ranks; alternative to the independent t-test"
    },
    {
      "id": "inference/mcnemar-test",
      "title": "McNemar's Test",
      "url": "tutorials/inference/mcnemar-test.html",
      "topic": "inference",
      "tags": [
        "mcnemar",
        "paired-binary",
        "discordant-pairs",
        "topic:inferential-statistics",
        "hypothesis-testing",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Paired / matched binary data: comparing two measurements on the same units, using discordant pairs only"
    },
    {
      "id": "inference/mixed-anova",
      "title": "Mixed ANOVA",
      "url": "tutorials/inference/mixed-anova.html",
      "topic": "inference",
      "tags": [
        "mixed-anova",
        "split-plot",
        "between-within",
        "topic:inferential-statistics",
        "hypothesis-testing"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Designs combining between-subjects and within-subjects factors, with the interaction as key"
    },
    {
      "id": "inference/multiple-comparisons-overview",
      "title": "Multiple Comparisons: Overview",
      "url": "tutorials/inference/multiple-comparisons-overview.html",
      "topic": "inference",
      "tags": [
        "multiple-testing",
        "fwer",
        "fdr",
        "correction",
        "topic:inferential-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Family-wise error vs false discovery rate and when each applies"
    },
    {
      "id": "inference/null-alternative-hypotheses",
      "title": "Null and Alternative Hypotheses",
      "url": "tutorials/inference/null-alternative-hypotheses.html",
      "topic": "inference",
      "tags": [
        "hypothesis",
        "null",
        "alternative",
        "one-sided",
        "topic:inferential-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Formulating H0 and H1, choosing one- vs two-sided tests, and avoiding post-hoc reformulation"
    },
    {
      "id": "inference/one-proportion-test",
      "title": "One-Proportion Test",
      "url": "tutorials/inference/one-proportion-test.html",
      "topic": "inference",
      "tags": [
        "proportion",
        "binomial",
        "wilson",
        "clopper-pearson",
        "topic:inferential-statistics",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Testing whether an observed proportion differs from a pre-specified reference; exact and score-based methods"
    },
    {
      "id": "inference/one-sample-t-test",
      "title": "One-Sample t-Test",
      "url": "tutorials/inference/one-sample-t-test.html",
      "topic": "inference",
      "tags": [
        "t-test",
        "one-sample",
        "mean-comparison",
        "topic:inferential-statistics",
        "hypothesis-testing"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Testing whether a sample mean equals a pre-specified reference value"
    },
    {
      "id": "inference/one-sample-z-test",
      "title": "One-Sample z-Test",
      "url": "tutorials/inference/one-sample-z-test.html",
      "topic": "inference",
      "tags": [
        "z-test",
        "known-variance",
        "normal-distribution",
        "topic:inferential-statistics",
        "hypothesis-testing"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Comparing a sample mean to a reference when the population variance is known -- rare in practice but pedagogically useful"
    },
    {
      "id": "inference/one-way-anova",
      "title": "One-Way ANOVA",
      "url": "tutorials/inference/one-way-anova.html",
      "topic": "inference",
      "tags": [
        "anova",
        "one-way",
        "f-test",
        "between-groups",
        "topic:inferential-statistics",
        "hypothesis-testing"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Comparing means across three or more independent groups via the F-test"
    },
    {
      "id": "inference/p-values-explained",
      "title": "P-Values Explained",
      "url": "tutorials/inference/p-values-explained.html",
      "topic": "inference",
      "tags": [
        "p-value",
        "significance",
        "hypothesis-testing",
        "topic:inferential-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Definition, correct interpretation, and the most common misinterpretations of the p-value"
    },
    {
      "id": "inference/paired-t-test",
      "title": "Paired t-Test",
      "url": "tutorials/inference/paired-t-test.html",
      "topic": "inference",
      "tags": [
        "paired-t-test",
        "dependent-samples",
        "within-subject",
        "topic:inferential-statistics",
        "hypothesis-testing"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Comparing two dependent measurements by applying a one-sample t-test to their differences"
    },
    {
      "id": "inference/pearson-correlation-test",
      "title": "Pearson Correlation Test",
      "url": "tutorials/inference/pearson-correlation-test.html",
      "topic": "inference",
      "tags": [
        "pearson",
        "correlation",
        "fisher-z",
        "t-test",
        "topic:inferential-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Testing whether the Pearson correlation between two continuous variables is non-zero"
    },
    {
      "id": "inference/permutation-tests",
      "title": "Permutation Tests",
      "url": "tutorials/inference/permutation-tests.html",
      "topic": "inference",
      "tags": [
        "permutation",
        "randomisation",
        "exact",
        "resampling",
        "topic:inferential-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Exact or Monte Carlo p-values via resampling under exchangeability"
    },
    {
      "id": "inference/post-hoc-tests-tukey",
      "title": "Post-Hoc Tests with Tukey HSD",
      "url": "tutorials/inference/post-hoc-tests-tukey.html",
      "topic": "inference",
      "tags": [
        "post-hoc",
        "tukey-hsd",
        "pairwise",
        "family-wise-error",
        "topic:inferential-statistics",
        "hypothesis-testing"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Pairwise comparisons after ANOVA with family-wise error control"
    },
    {
      "id": "inference/repeated-measures-anova",
      "title": "Repeated-Measures ANOVA",
      "url": "tutorials/inference/repeated-measures-anova.html",
      "topic": "inference",
      "tags": [
        "rmanova",
        "within-subjects",
        "sphericity",
        "mauchly",
        "topic:inferential-statistics",
        "hypothesis-testing",
        "longitudinal-and-mixed-models"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Within-subjects analysis of variance with sphericity checks and corrections"
    },
    {
      "id": "inference/runs-test",
      "title": "The Runs Test",
      "url": "tutorials/inference/runs-test.html",
      "topic": "inference",
      "tags": [
        "runs-test",
        "randomness",
        "sequence",
        "topic:inferential-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Testing randomness of a binary or dichotomised sequence via run lengths"
    },
    {
      "id": "inference/shapiro-wilk-test",
      "title": "Shapiro-Wilk Normality Test",
      "url": "tutorials/inference/shapiro-wilk-test.html",
      "topic": "inference",
      "tags": [
        "shapiro-wilk",
        "normality",
        "small-sample",
        "topic:inferential-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The most powerful commonly used test for normality in small to moderate samples"
    },
    {
      "id": "inference/sign-test",
      "title": "The Sign Test",
      "url": "tutorials/inference/sign-test.html",
      "topic": "inference",
      "tags": [
        "sign-test",
        "median",
        "non-parametric",
        "topic:inferential-statistics",
        "hypothesis-testing"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Testing a median or paired difference using only the direction of each observation"
    },
    {
      "id": "inference/spearman-correlation-test",
      "title": "Spearman Rank Correlation Test",
      "url": "tutorials/inference/spearman-correlation-test.html",
      "topic": "inference",
      "tags": [
        "spearman",
        "rank-correlation",
        "monotonic",
        "non-parametric",
        "topic:inferential-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Non-parametric correlation test for monotonic association between two variables"
    },
    {
      "id": "inference/two-proportion-test",
      "title": "Two-Proportion Test",
      "url": "tutorials/inference/two-proportion-test.html",
      "topic": "inference",
      "tags": [
        "two-proportion",
        "z-test",
        "risk-difference",
        "relative-risk",
        "topic:inferential-statistics",
        "hypothesis-testing",
        "effect-size",
        "categorical-data",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Comparing two independent proportions; z-test and chi-squared equivalence, with risk difference and relative risk"
    },
    {
      "id": "inference/two-sample-t-test",
      "title": "Two-Sample t-Test",
      "url": "tutorials/inference/two-sample-t-test.html",
      "topic": "inference",
      "tags": [
        "t-test",
        "welch-test",
        "effect-size",
        "cohen-d",
        "group-comparison",
        "topic:inferential-statistics",
        "hypothesis-testing"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Comparing means between two independent groups with Student's and Welch's t-tests, including assumption checks and effect sizes"
    },
    {
      "id": "inference/two-way-anova",
      "title": "Two-Way ANOVA",
      "url": "tutorials/inference/two-way-anova.html",
      "topic": "inference",
      "tags": [
        "anova",
        "two-way",
        "interaction",
        "factorial",
        "topic:inferential-statistics",
        "hypothesis-testing"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Two between-subjects factors: main effects and interaction"
    },
    {
      "id": "inference/type-i-type-ii-errors",
      "title": "Type I and Type II Errors",
      "url": "tutorials/inference/type-i-type-ii-errors.html",
      "topic": "inference",
      "tags": [
        "type-i",
        "type-ii",
        "alpha",
        "beta",
        "power",
        "topic:inferential-statistics",
        "power-and-sample-size"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Rejecting a true null (alpha) and failing to reject a false null (beta); power and the trade-off"
    },
    {
      "id": "inference/welch-t-test",
      "title": "Welch's t-Test",
      "url": "tutorials/inference/welch-t-test.html",
      "topic": "inference",
      "tags": [
        "welch",
        "t-test",
        "unequal-variances",
        "satterthwaite",
        "topic:inferential-statistics",
        "hypothesis-testing"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Two-sample t-test that does not assume equal variances; R's default"
    },
    {
      "id": "inference/wilcoxon-signed-rank",
      "title": "Wilcoxon Signed-Rank Test",
      "url": "tutorials/inference/wilcoxon-signed-rank.html",
      "topic": "inference",
      "tags": [
        "wilcoxon",
        "signed-rank",
        "paired",
        "non-parametric",
        "topic:inferential-statistics",
        "hypothesis-testing"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Non-parametric paired-sample test based on signed ranks"
    },
    {
      "id": "machine-learning/anomaly-detection",
      "title": "Anomaly Detection",
      "url": "tutorials/machine-learning/anomaly-detection.html",
      "topic": "machine-learning",
      "tags": [
        "anomaly-detection",
        "one-class-svm",
        "autoencoder",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Unsupervised methods for flagging rare, unusual observations"
    },
    {
      "id": "machine-learning/bagging",
      "title": "Bagging",
      "url": "tutorials/machine-learning/bagging.html",
      "topic": "machine-learning",
      "tags": [
        "bagging",
        "ensemble",
        "bootstrap",
        "topic:machine-learning",
        "non-parametric"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Bootstrap aggregation: variance reduction through averaging"
    },
    {
      "id": "machine-learning/bias-variance-tradeoff",
      "title": "The Bias-Variance Tradeoff",
      "url": "tutorials/machine-learning/bias-variance-tradeoff.html",
      "topic": "machine-learning",
      "tags": [
        "bias-variance",
        "decomposition",
        "flexibility",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Decomposition of prediction error into structural and sampling components"
    },
    {
      "id": "machine-learning/calibration-plots",
      "title": "Calibration Plots",
      "url": "tutorials/machine-learning/calibration-plots.html",
      "topic": "machine-learning",
      "tags": [
        "calibration",
        "reliability",
        "probabilities",
        "topic:machine-learning",
        "agreement-and-reliability",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Reliability diagrams: do predicted probabilities match observed frequencies?"
    },
    {
      "id": "machine-learning/catboost",
      "title": "CatBoost",
      "url": "tutorials/machine-learning/catboost.html",
      "topic": "machine-learning",
      "tags": [
        "catboost",
        "categorical",
        "boosting",
        "topic:machine-learning",
        "categorical-data",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Gradient boosting with native categorical handling and ordered target encoding"
    },
    {
      "id": "machine-learning/class-weights-imbalanced",
      "title": "Class Weights for Imbalanced Data",
      "url": "tutorials/machine-learning/class-weights-imbalanced.html",
      "topic": "machine-learning",
      "tags": [
        "class-weights",
        "imbalanced",
        "loss",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Loss-based weighting as an alternative to resampling"
    },
    {
      "id": "machine-learning/clustering-dbscan-ml",
      "title": "DBSCAN as an ML Tool",
      "url": "tutorials/machine-learning/clustering-dbscan-ml.html",
      "topic": "machine-learning",
      "tags": [
        "dbscan",
        "clustering",
        "anomaly-detection",
        "topic:machine-learning",
        "robust-statistics",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Density-based clustering and outlier detection in one step"
    },
    {
      "id": "machine-learning/clustering-kmeans-ml",
      "title": "k-Means as an ML Tool",
      "url": "tutorials/machine-learning/clustering-kmeans-ml.html",
      "topic": "machine-learning",
      "tags": [
        "kmeans",
        "clustering",
        "feature-engineering",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Using k-means for feature engineering, quantisation, and pre-segmentation"
    },
    {
      "id": "machine-learning/cnn-introduction",
      "title": "Convolutional Neural Networks: Introduction",
      "url": "tutorials/machine-learning/cnn-introduction.html",
      "topic": "machine-learning",
      "tags": [
        "cnn",
        "convolution",
        "pooling",
        "topic:machine-learning",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Spatial filters and pooling for image and signal learning"
    },
    {
      "id": "machine-learning/cross-validation",
      "title": "Cross-Validation",
      "url": "tutorials/machine-learning/cross-validation.html",
      "topic": "machine-learning",
      "tags": [
        "cross-validation",
        "model-evaluation",
        "tidymodels",
        "resampling",
        "topic:machine-learning",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Estimating out-of-sample predictive performance honestly with k-fold, repeated, nested, and leave-one-out cross-validation"
    },
    {
      "id": "machine-learning/decision-trees-cart",
      "title": "Decision Trees (CART)",
      "url": "tutorials/machine-learning/decision-trees-cart.html",
      "topic": "machine-learning",
      "tags": [
        "tree",
        "cart",
        "rpart",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Recursive binary partitioning with Gini or variance splits"
    },
    {
      "id": "machine-learning/dropout-early-stopping",
      "title": "Dropout and Early Stopping",
      "url": "tutorials/machine-learning/dropout-early-stopping.html",
      "topic": "machine-learning",
      "tags": [
        "dropout",
        "early-stopping",
        "regularisation",
        "topic:machine-learning",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Two of the most effective neural-network regularisation techniques"
    },
    {
      "id": "machine-learning/extremely-randomized-trees",
      "title": "Extremely Randomised Trees",
      "url": "tutorials/machine-learning/extremely-randomized-trees.html",
      "topic": "machine-learning",
      "tags": [
        "extra-trees",
        "randomisation",
        "ensemble",
        "topic:machine-learning",
        "experimental-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Extra Trees: random thresholds for faster and more regularised ensembles"
    },
    {
      "id": "machine-learning/feature-engineering",
      "title": "Feature Engineering",
      "url": "tutorials/machine-learning/feature-engineering.html",
      "topic": "machine-learning",
      "tags": [
        "feature-engineering",
        "transformations",
        "interactions",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Transformations, interactions, and encodings to expose signal to models"
    },
    {
      "id": "machine-learning/feature-selection",
      "title": "Feature Selection",
      "url": "tutorials/machine-learning/feature-selection.html",
      "topic": "machine-learning",
      "tags": [
        "feature-selection",
        "filter",
        "wrapper",
        "embedded",
        "topic:machine-learning",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Filter, wrapper, and embedded strategies to reduce the feature space"
    },
    {
      "id": "machine-learning/feedforward-networks-torch",
      "title": "Feedforward Networks in torch",
      "url": "tutorials/machine-learning/feedforward-networks-torch.html",
      "topic": "machine-learning",
      "tags": [
        "torch",
        "deep-learning",
        "feedforward",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Building and training deep feedforward networks in R with the torch package"
    },
    {
      "id": "machine-learning/gradient-boosting",
      "title": "Gradient Boosting",
      "url": "tutorials/machine-learning/gradient-boosting.html",
      "topic": "machine-learning",
      "tags": [
        "boosting",
        "gbm",
        "stagewise",
        "topic:machine-learning",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Stagewise additive modelling with gradient descent on loss"
    },
    {
      "id": "machine-learning/isolation-forest",
      "title": "Isolation Forest",
      "url": "tutorials/machine-learning/isolation-forest.html",
      "topic": "machine-learning",
      "tags": [
        "isolation-forest",
        "anomaly-detection",
        "trees",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Tree-based unsupervised anomaly detection via path length"
    },
    {
      "id": "machine-learning/isotonic-regression",
      "title": "Isotonic Regression Calibration",
      "url": "tutorials/machine-learning/isotonic-regression.html",
      "topic": "machine-learning",
      "tags": [
        "isotonic",
        "calibration",
        "monotone",
        "topic:machine-learning",
        "regression",
        "non-parametric",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Non-parametric monotone calibration via pool-adjacent-violators"
    },
    {
      "id": "machine-learning/knn-classification",
      "title": "k-Nearest Neighbours Classification",
      "url": "tutorials/machine-learning/knn-classification.html",
      "topic": "machine-learning",
      "tags": [
        "knn",
        "classification",
        "distance",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Instance-based learning with majority voting among nearest neighbours"
    },
    {
      "id": "machine-learning/knn-regression",
      "title": "k-Nearest Neighbours Regression",
      "url": "tutorials/machine-learning/knn-regression.html",
      "topic": "machine-learning",
      "tags": [
        "knn",
        "regression",
        "nonparametric",
        "topic:machine-learning",
        "non-parametric"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Local averaging for non-parametric regression"
    },
    {
      "id": "machine-learning/lightgbm",
      "title": "LightGBM",
      "url": "tutorials/machine-learning/lightgbm.html",
      "topic": "machine-learning",
      "tags": [
        "lightgbm",
        "boosting",
        "histogram",
        "topic:machine-learning",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Histogram-based leaf-wise gradient boosting for large datasets"
    },
    {
      "id": "machine-learning/lime-explanations",
      "title": "LIME Explanations",
      "url": "tutorials/machine-learning/lime-explanations.html",
      "topic": "machine-learning",
      "tags": [
        "lime",
        "interpretability",
        "surrogate",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Local Interpretable Model-Agnostic Explanations via surrogate linear models"
    },
    {
      "id": "machine-learning/linear-discriminant-ml",
      "title": "Linear Discriminant as ML",
      "url": "tutorials/machine-learning/linear-discriminant-ml.html",
      "topic": "machine-learning",
      "tags": [
        "lda",
        "classifier",
        "gaussian",
        "topic:machine-learning",
        "multivariate-analysis",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "LDA as a classifier with shared Gaussian class-conditional covariance"
    },
    {
      "id": "machine-learning/logistic-regression-ml",
      "title": "Logistic Regression as ML",
      "url": "tutorials/machine-learning/logistic-regression-ml.html",
      "topic": "machine-learning",
      "tags": [
        "logistic",
        "classifier",
        "regularisation",
        "topic:machine-learning",
        "regression",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Linear classifier with log-loss and L1/L2 regularisation"
    },
    {
      "id": "machine-learning/mlr3-workflow",
      "title": "A Complete mlr3 Workflow",
      "url": "tutorials/machine-learning/mlr3-workflow.html",
      "topic": "machine-learning",
      "tags": [
        "mlr3",
        "workflow",
        "tasks",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "End-to-end modelling with mlr3: tasks, learners, resamplings, and tuning"
    },
    {
      "id": "machine-learning/naive-bayes",
      "title": "Naive Bayes",
      "url": "tutorials/machine-learning/naive-bayes.html",
      "topic": "machine-learning",
      "tags": [
        "naive-bayes",
        "probabilistic",
        "classification",
        "topic:machine-learning",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Probabilistic classification under conditional independence"
    },
    {
      "id": "machine-learning/nested-cv",
      "title": "Nested Cross-Validation",
      "url": "tutorials/machine-learning/nested-cv.html",
      "topic": "machine-learning",
      "tags": [
        "nested-cv",
        "tuning",
        "evaluation",
        "topic:machine-learning",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Honest model evaluation with separate loops for tuning and assessment"
    },
    {
      "id": "machine-learning/neural-networks-intro",
      "title": "Neural Networks: Introduction",
      "url": "tutorials/machine-learning/neural-networks-intro.html",
      "topic": "machine-learning",
      "tags": [
        "neural-net",
        "perceptron",
        "deep-learning",
        "topic:machine-learning",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "From perceptrons to multi-layer networks: weights, layers, and activation functions"
    },
    {
      "id": "machine-learning/partial-dependence-plots",
      "title": "Partial Dependence Plots",
      "url": "tutorials/machine-learning/partial-dependence-plots.html",
      "topic": "machine-learning",
      "tags": [
        "pdp",
        "partial-dependence",
        "marginal",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Marginal effect of a feature on model predictions"
    },
    {
      "id": "machine-learning/platt-scaling",
      "title": "Platt Scaling",
      "url": "tutorials/machine-learning/platt-scaling.html",
      "topic": "machine-learning",
      "tags": [
        "platt-scaling",
        "calibration",
        "logistic",
        "topic:machine-learning",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Logistic recalibration: mapping raw scores to calibrated probabilities"
    },
    {
      "id": "machine-learning/random-forest",
      "title": "Random Forests",
      "url": "tutorials/machine-learning/random-forest.html",
      "topic": "machine-learning",
      "tags": [
        "random-forest",
        "bagging",
        "ensemble",
        "topic:machine-learning",
        "machine-learning-methods",
        "robust-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Bagging decision trees with random feature subsets for robust ensembles"
    },
    {
      "id": "machine-learning/recipes-preprocessing",
      "title": "Preprocessing with recipes",
      "url": "tutorials/machine-learning/recipes-preprocessing.html",
      "topic": "machine-learning",
      "tags": [
        "recipes",
        "preprocessing",
        "tidymodels",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Leak-free feature engineering in tidymodels with recipes"
    },
    {
      "id": "machine-learning/regularization-ridge-lasso-ml",
      "title": "Regularisation in ML",
      "url": "tutorials/machine-learning/regularization-ridge-lasso-ml.html",
      "topic": "machine-learning",
      "tags": [
        "regularisation",
        "ridge",
        "lasso",
        "elastic-net",
        "topic:machine-learning",
        "regression",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "L2 ridge, L1 lasso, and elastic net for controlling model complexity"
    },
    {
      "id": "machine-learning/rnn-lstm-intro",
      "title": "RNNs and LSTMs: Introduction",
      "url": "tutorials/machine-learning/rnn-lstm-intro.html",
      "topic": "machine-learning",
      "tags": [
        "rnn",
        "lstm",
        "sequence",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Recurrent networks for sequence modelling with gated memory"
    },
    {
      "id": "machine-learning/shap-values",
      "title": "SHAP Values",
      "url": "tutorials/machine-learning/shap-values.html",
      "topic": "machine-learning",
      "tags": [
        "shap",
        "shapley",
        "interpretability",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Game-theoretic local feature attributions via Shapley values"
    },
    {
      "id": "machine-learning/smote-imbalanced",
      "title": "SMOTE for Imbalanced Classes",
      "url": "tutorials/machine-learning/smote-imbalanced.html",
      "topic": "machine-learning",
      "tags": [
        "smote",
        "imbalanced",
        "oversampling",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Synthetic Minority Oversampling Technique for class-imbalanced classification"
    },
    {
      "id": "machine-learning/stacking-ensembles",
      "title": "Stacking Ensembles",
      "url": "tutorials/machine-learning/stacking-ensembles.html",
      "topic": "machine-learning",
      "tags": [
        "stacking",
        "ensemble",
        "meta-learner",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Combining heterogeneous base models via a meta-learner"
    },
    {
      "id": "machine-learning/supervised-learning-overview",
      "title": "Supervised Learning: Overview",
      "url": "tutorials/machine-learning/supervised-learning-overview.html",
      "topic": "machine-learning",
      "tags": [
        "supervised-learning",
        "loss",
        "generalisation",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Framework, loss functions, generalisation, and the bias-variance decomposition"
    },
    {
      "id": "machine-learning/svm-kernel",
      "title": "Kernel SVMs",
      "url": "tutorials/machine-learning/svm-kernel.html",
      "topic": "machine-learning",
      "tags": [
        "svm",
        "kernel",
        "rbf",
        "topic:machine-learning",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Non-linear classification via the kernel trick with RBF and polynomial kernels"
    },
    {
      "id": "machine-learning/svm-linear",
      "title": "Linear Support Vector Machines",
      "url": "tutorials/machine-learning/svm-linear.html",
      "topic": "machine-learning",
      "tags": [
        "svm",
        "margin",
        "linear",
        "topic:machine-learning",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Large-margin classifiers with soft-margin slack and the C parameter"
    },
    {
      "id": "machine-learning/tidymodels-workflow",
      "title": "A Complete tidymodels Workflow",
      "url": "tutorials/machine-learning/tidymodels-workflow.html",
      "topic": "machine-learning",
      "tags": [
        "tidymodels",
        "workflow",
        "recipes",
        "parsnip",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "End-to-end modelling in tidymodels: recipes, parsnip, workflows, and tuning"
    },
    {
      "id": "machine-learning/train-test-split",
      "title": "Train-Test Splits",
      "url": "tutorials/machine-learning/train-test-split.html",
      "topic": "machine-learning",
      "tags": [
        "holdout",
        "split",
        "stratification",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Holdout evaluation, stratification, and honest generalisation estimates"
    },
    {
      "id": "machine-learning/transformer-overview",
      "title": "Transformers: Overview",
      "url": "tutorials/machine-learning/transformer-overview.html",
      "topic": "machine-learning",
      "tags": [
        "transformer",
        "attention",
        "seq2seq",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Self-attention architectures underlying modern NLP and beyond"
    },
    {
      "id": "machine-learning/variable-importance",
      "title": "Variable Importance",
      "url": "tutorials/machine-learning/variable-importance.html",
      "topic": "machine-learning",
      "tags": [
        "importance",
        "permutation",
        "impurity",
        "topic:machine-learning"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Impurity-based and permutation importance for tree ensembles and beyond"
    },
    {
      "id": "machine-learning/xgboost",
      "title": "XGBoost",
      "url": "tutorials/machine-learning/xgboost.html",
      "topic": "machine-learning",
      "tags": [
        "xgboost",
        "boosting",
        "regularisation",
        "topic:machine-learning",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Regularised gradient boosting with sparsity awareness and early stopping"
    },
    {
      "id": "meta-analysis/bivariate-sroc",
      "title": "Bivariate SROC Curves",
      "url": "tutorials/meta-analysis/bivariate-sroc.html",
      "topic": "meta-analysis",
      "tags": [
        "sroc",
        "bivariate",
        "hierarchical",
        "topic:meta-analysis",
        "diagnostic-accuracy",
        "exploratory-and-descriptive",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Hierarchical summary ROC modelling for diagnostic-accuracy meta-analysis"
    },
    {
      "id": "meta-analysis/correlation-pooling",
      "title": "Pooling Correlation Coefficients",
      "url": "tutorials/meta-analysis/correlation-pooling.html",
      "topic": "meta-analysis",
      "tags": [
        "correlation",
        "fisher-z",
        "pooling",
        "topic:meta-analysis",
        "hypothesis-testing",
        "categorical-data",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Fisher z transformation for meta-analysis of correlations"
    },
    {
      "id": "meta-analysis/cumulative-meta-analysis",
      "title": "Cumulative Meta-Analysis",
      "url": "tutorials/meta-analysis/cumulative-meta-analysis.html",
      "topic": "meta-analysis",
      "tags": [
        "cumulative",
        "sequential",
        "temporal",
        "topic:meta-analysis",
        "meta-analysis-methods",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sequentially pooling studies in the order they were published"
    },
    {
      "id": "meta-analysis/diagnostic-test-meta",
      "title": "Meta-Analysis of Diagnostic Accuracy",
      "url": "tutorials/meta-analysis/diagnostic-test-meta.html",
      "topic": "meta-analysis",
      "tags": [
        "diagnostic",
        "bivariate",
        "hsroc",
        "topic:meta-analysis",
        "diagnostic-accuracy",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Bivariate and HSROC models for pooling diagnostic test performance"
    },
    {
      "id": "meta-analysis/effect-size-conversion",
      "title": "Converting Between Effect Sizes",
      "url": "tutorials/meta-analysis/effect-size-conversion.html",
      "topic": "meta-analysis",
      "tags": [
        "effect-size",
        "conversion",
        "d-to-r",
        "topic:meta-analysis",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Moving between standardised mean differences, correlations, and odds ratios"
    },
    {
      "id": "meta-analysis/egger-test",
      "title": "Egger's Test for Funnel Asymmetry",
      "url": "tutorials/meta-analysis/egger-test.html",
      "topic": "meta-analysis",
      "tags": [
        "egger",
        "publication-bias",
        "asymmetry",
        "topic:meta-analysis",
        "regression",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Formal regression test for small-study effects and publication bias"
    },
    {
      "id": "meta-analysis/fixed-vs-random-effects",
      "title": "Fixed-Effect vs. Random-Effects Meta-Analysis",
      "url": "tutorials/meta-analysis/fixed-vs-random-effects.html",
      "topic": "meta-analysis",
      "tags": [
        "meta-analysis",
        "fixed-effect",
        "random-effects",
        "heterogeneity",
        "topic:meta-analysis",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Two ways to pool study-level effect sizes, and the consequences of each for inference and interpretation"
    },
    {
      "id": "meta-analysis/forest-plot-meta",
      "title": "Forest Plots in Meta-Analysis",
      "url": "tutorials/meta-analysis/forest-plot-meta.html",
      "topic": "meta-analysis",
      "tags": [
        "forest-plot",
        "pooled",
        "weights",
        "topic:meta-analysis",
        "exploratory-and-descriptive",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Standard visualisation of per-study effects, weights, and the pooled estimate"
    },
    {
      "id": "meta-analysis/funnel-plot-meta",
      "title": "Funnel Plots",
      "url": "tutorials/meta-analysis/funnel-plot-meta.html",
      "topic": "meta-analysis",
      "tags": [
        "funnel-plot",
        "publication-bias",
        "asymmetry",
        "topic:meta-analysis",
        "exploratory-and-descriptive",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Visual inspection for small-study effects and publication bias"
    },
    {
      "id": "meta-analysis/heterogeneity-q-i2",
      "title": "Quantifying Heterogeneity: Q and I^2",
      "url": "tutorials/meta-analysis/heterogeneity-q-i2.html",
      "topic": "meta-analysis",
      "tags": [
        "heterogeneity",
        "i-squared",
        "cochran-q",
        "topic:meta-analysis",
        "categorical-data",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Cochran's Q, I-squared, and their interpretation in meta-analysis"
    },
    {
      "id": "meta-analysis/ipd-meta-analysis",
      "title": "Individual Participant Data Meta-Analysis",
      "url": "tutorials/meta-analysis/ipd-meta-analysis.html",
      "topic": "meta-analysis",
      "tags": [
        "ipd",
        "two-stage",
        "one-stage",
        "topic:meta-analysis",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Two-stage vs one-stage analysis of raw data pooled across studies"
    },
    {
      "id": "meta-analysis/leave-one-out",
      "title": "Leave-One-Out Meta-Analysis",
      "url": "tutorials/meta-analysis/leave-one-out.html",
      "topic": "meta-analysis",
      "tags": [
        "leave-one-out",
        "sensitivity",
        "influence",
        "topic:meta-analysis",
        "diagnostic-accuracy",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sensitivity of the pooled estimate to each individual study"
    },
    {
      "id": "meta-analysis/log-odds-ratio-pooling",
      "title": "Pooling Log-Odds Ratios",
      "url": "tutorials/meta-analysis/log-odds-ratio-pooling.html",
      "topic": "meta-analysis",
      "tags": [
        "log-or",
        "pooling",
        "binary",
        "topic:meta-analysis",
        "effect-size",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Fixed and random-effects meta-analysis of binary outcomes on the log scale"
    },
    {
      "id": "meta-analysis/meta-regression",
      "title": "Meta-Regression",
      "url": "tutorials/meta-analysis/meta-regression.html",
      "topic": "meta-analysis",
      "tags": [
        "meta-regression",
        "moderator",
        "heterogeneity",
        "topic:meta-analysis",
        "regression",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Explaining between-study heterogeneity with study-level covariates"
    },
    {
      "id": "meta-analysis/network-meta-analysis-intro",
      "title": "Network Meta-Analysis: Introduction",
      "url": "tutorials/meta-analysis/network-meta-analysis-intro.html",
      "topic": "meta-analysis",
      "tags": [
        "network-meta-analysis",
        "indirect",
        "mtc",
        "topic:meta-analysis",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Combining direct and indirect comparisons across multiple treatments"
    },
    {
      "id": "meta-analysis/nma-consistency",
      "title": "NMA Consistency and Inconsistency",
      "url": "tutorials/meta-analysis/nma-consistency.html",
      "topic": "meta-analysis",
      "tags": [
        "consistency",
        "nma",
        "inconsistency",
        "topic:meta-analysis",
        "agreement-and-reliability",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Loop-specific and global tests for direct-indirect evidence agreement"
    },
    {
      "id": "meta-analysis/nma-league-tables",
      "title": "NMA League Tables",
      "url": "tutorials/meta-analysis/nma-league-tables.html",
      "topic": "meta-analysis",
      "tags": [
        "league-table",
        "nma",
        "pairwise",
        "topic:meta-analysis",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Pairwise treatment contrasts in a compact triangular grid"
    },
    {
      "id": "meta-analysis/nma-sucra",
      "title": "SUCRA Rankings",
      "url": "tutorials/meta-analysis/nma-sucra.html",
      "topic": "meta-analysis",
      "tags": [
        "sucra",
        "ranking",
        "nma",
        "topic:meta-analysis",
        "meta-analysis-methods",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Surface Under the Cumulative Ranking curve for treatment comparisons"
    },
    {
      "id": "meta-analysis/prediction-interval-meta",
      "title": "Prediction Intervals in Meta-Analysis",
      "url": "tutorials/meta-analysis/prediction-interval-meta.html",
      "topic": "meta-analysis",
      "tags": [
        "prediction-interval",
        "heterogeneity",
        "random-effects",
        "topic:meta-analysis",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The plausible range of a future study's true effect under random-effects"
    },
    {
      "id": "meta-analysis/risk-ratio-pooling",
      "title": "Pooling Risk Ratios",
      "url": "tutorials/meta-analysis/risk-ratio-pooling.html",
      "topic": "meta-analysis",
      "tags": [
        "risk-ratio",
        "pooling",
        "binary",
        "topic:meta-analysis",
        "effect-size",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Meta-analysis of binary outcomes on the risk-ratio scale"
    },
    {
      "id": "meta-analysis/selection-models-bias",
      "title": "Selection Models for Publication Bias",
      "url": "tutorials/meta-analysis/selection-models-bias.html",
      "topic": "meta-analysis",
      "tags": [
        "selection",
        "publication-bias",
        "weighted",
        "topic:meta-analysis",
        "meta-analysis-methods",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Weighted-distribution models for adjusting meta-analysis for selection"
    },
    {
      "id": "meta-analysis/smd-hedges-g",
      "title": "Standardised Mean Difference: Hedges' g",
      "url": "tutorials/meta-analysis/smd-hedges-g.html",
      "topic": "meta-analysis",
      "tags": [
        "hedges-g",
        "smd",
        "effect-size",
        "topic:meta-analysis",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Small-sample-corrected standardised effect size for continuous outcomes"
    },
    {
      "id": "meta-analysis/subgroup-meta-analysis",
      "title": "Subgroup Meta-Analysis",
      "url": "tutorials/meta-analysis/subgroup-meta-analysis.html",
      "topic": "meta-analysis",
      "tags": [
        "subgroup",
        "heterogeneity",
        "moderator",
        "topic:meta-analysis",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Pooling within strata and testing for between-subgroup heterogeneity"
    },
    {
      "id": "meta-analysis/tau-squared-estimation",
      "title": "Tau-Squared Estimation",
      "url": "tutorials/meta-analysis/tau-squared-estimation.html",
      "topic": "meta-analysis",
      "tags": [
        "tau-squared",
        "REML",
        "DerSimonian-Laird",
        "topic:meta-analysis",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Estimators of between-study variance: DL, REML, PM, SJ, EB"
    },
    {
      "id": "meta-analysis/trim-and-fill",
      "title": "Trim-and-Fill",
      "url": "tutorials/meta-analysis/trim-and-fill.html",
      "topic": "meta-analysis",
      "tags": [
        "trim-and-fill",
        "publication-bias",
        "sensitivity",
        "topic:meta-analysis",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Adjusting the pooled estimate for apparent funnel asymmetry"
    },
    {
      "id": "multivariate/bray-curtis",
      "title": "Bray-Curtis Dissimilarity",
      "url": "tutorials/multivariate/bray-curtis.html",
      "topic": "multivariate",
      "tags": [
        "bray-curtis",
        "ecological",
        "abundance",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The standard ecological distance metric for species-abundance community data"
    },
    {
      "id": "multivariate/canonical-correlation",
      "title": "Canonical Correlation Analysis",
      "url": "tutorials/multivariate/canonical-correlation.html",
      "topic": "multivariate",
      "tags": [
        "cca",
        "canonical-correlation",
        "multivariate",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Identifying pairs of linear combinations that maximise correlation between two variable sets"
    },
    {
      "id": "multivariate/cluster-validation-silhouette",
      "title": "Cluster Validation: Silhouette",
      "url": "tutorials/multivariate/cluster-validation-silhouette.html",
      "topic": "multivariate",
      "tags": [
        "silhouette",
        "cluster-validation",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Measuring how well each observation fits its assigned cluster relative to neighbouring clusters"
    },
    {
      "id": "multivariate/correspondence-analysis",
      "title": "Correspondence Analysis",
      "url": "tutorials/multivariate/correspondence-analysis.html",
      "topic": "multivariate",
      "tags": [
        "correspondence-analysis",
        "ca",
        "biplot",
        "topic:multivariate-methods",
        "hypothesis-testing",
        "categorical-data",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Low-dimensional biplot representation of contingency tables via chi-squared distance"
    },
    {
      "id": "multivariate/dbscan-clustering",
      "title": "DBSCAN Clustering",
      "url": "tutorials/multivariate/dbscan-clustering.html",
      "topic": "multivariate",
      "tags": [
        "dbscan",
        "density",
        "clustering",
        "topic:multivariate-methods",
        "multivariate-analysis",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Density-based clustering with automatic noise detection and arbitrary cluster shapes"
    },
    {
      "id": "multivariate/distance-measures",
      "title": "Distance and Dissimilarity Measures",
      "url": "tutorials/multivariate/distance-measures.html",
      "topic": "multivariate",
      "tags": [
        "distance",
        "metric",
        "dissimilarity",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Euclidean, Manhattan, Minkowski, Canberra, and their use in clustering / MDS"
    },
    {
      "id": "multivariate/elbow-method",
      "title": "The Elbow Method",
      "url": "tutorials/multivariate/elbow-method.html",
      "topic": "multivariate",
      "tags": [
        "elbow",
        "wss",
        "cluster-number",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Choosing the number of clusters by identifying a bend in the within-cluster sum of squares curve"
    },
    {
      "id": "multivariate/factor-analysis-confirmatory",
      "title": "Confirmatory Factor Analysis",
      "url": "tutorials/multivariate/factor-analysis-confirmatory.html",
      "topic": "multivariate",
      "tags": [
        "cfa",
        "lavaan",
        "structural-equation",
        "fit-indices",
        "topic:multivariate-methods",
        "hypothesis-testing",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Testing a pre-specified factor structure against data, with chi-squared and incremental fit indices"
    },
    {
      "id": "multivariate/factor-analysis-exploratory",
      "title": "Exploratory Factor Analysis",
      "url": "tutorials/multivariate/factor-analysis-exploratory.html",
      "topic": "multivariate",
      "tags": [
        "efa",
        "factor-analysis",
        "loading",
        "rotation",
        "topic:multivariate-methods",
        "multivariate-analysis",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Identifying latent factors from correlated manifest variables, with extraction, rotation, and number-of-factors decisions"
    },
    {
      "id": "multivariate/factor-rotation",
      "title": "Factor Rotation",
      "url": "tutorials/multivariate/factor-rotation.html",
      "topic": "multivariate",
      "tags": [
        "rotation",
        "varimax",
        "promax",
        "oblimin",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Orthogonal and oblique rotations to make factor loadings interpretable"
    },
    {
      "id": "multivariate/gap-statistic",
      "title": "The Gap Statistic",
      "url": "tutorials/multivariate/gap-statistic.html",
      "topic": "multivariate",
      "tags": [
        "gap-statistic",
        "cluster-number",
        "bootstrap",
        "topic:multivariate-methods",
        "multivariate-analysis",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Choosing the number of clusters via comparison with a null (uniform) distribution"
    },
    {
      "id": "multivariate/gaussian-mixture-models",
      "title": "Gaussian Mixture Models",
      "url": "tutorials/multivariate/gaussian-mixture-models.html",
      "topic": "multivariate",
      "tags": [
        "gmm",
        "mixture-model",
        "em-algorithm",
        "mclust",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Model-based clustering via mixtures of multivariate normals, fitted by EM"
    },
    {
      "id": "multivariate/hierarchical-clustering",
      "title": "Hierarchical Clustering",
      "url": "tutorials/multivariate/hierarchical-clustering.html",
      "topic": "multivariate",
      "tags": [
        "hierarchical",
        "ward",
        "linkage",
        "dendrogram",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Agglomerative (bottom-up) clustering with various linkage criteria and dendrogram display"
    },
    {
      "id": "multivariate/ica-independent-component-analysis",
      "title": "Independent Component Analysis",
      "url": "tutorials/multivariate/ica-independent-component-analysis.html",
      "topic": "multivariate",
      "tags": [
        "ica",
        "independent-components",
        "fastica",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Signal separation: recovering independent latent sources from linear mixtures"
    },
    {
      "id": "multivariate/jaccard-dissimilarity",
      "title": "Jaccard and Dice Dissimilarity",
      "url": "tutorials/multivariate/jaccard-dissimilarity.html",
      "topic": "multivariate",
      "tags": [
        "jaccard",
        "dice",
        "binary-distance",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Distance measures for binary / presence-absence data"
    },
    {
      "id": "multivariate/kernel-pca",
      "title": "Kernel PCA",
      "url": "tutorials/multivariate/kernel-pca.html",
      "topic": "multivariate",
      "tags": [
        "kernel-pca",
        "non-linear",
        "rbf",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Non-linear dimensionality reduction via PCA in an implicit feature space"
    },
    {
      "id": "multivariate/kmeans-clustering",
      "title": "k-Means Clustering",
      "url": "tutorials/multivariate/kmeans-clustering.html",
      "topic": "multivariate",
      "tags": [
        "kmeans",
        "clustering",
        "partitioning",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Partitioning observations into k clusters by minimising within-cluster sum of squares"
    },
    {
      "id": "multivariate/linear-discriminant-analysis",
      "title": "Linear Discriminant Analysis",
      "url": "tutorials/multivariate/linear-discriminant-analysis.html",
      "topic": "multivariate",
      "tags": [
        "lda",
        "classification",
        "fisher",
        "topic:multivariate-methods",
        "multivariate-analysis",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Classifier assuming multivariate normal within-class distributions with equal covariance"
    },
    {
      "id": "multivariate/mahalanobis-distance",
      "title": "Mahalanobis Distance",
      "url": "tutorials/multivariate/mahalanobis-distance.html",
      "topic": "multivariate",
      "tags": [
        "mahalanobis",
        "multivariate-outlier",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Scale- and correlation-aware distance for multivariate data"
    },
    {
      "id": "multivariate/manova",
      "title": "MANOVA",
      "url": "tutorials/multivariate/manova.html",
      "topic": "multivariate",
      "tags": [
        "manova",
        "wilks-lambda",
        "multivariate-test",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Multivariate analysis of variance: testing group differences across multiple correlated outcomes jointly"
    },
    {
      "id": "multivariate/mds-metric",
      "title": "Metric Multidimensional Scaling",
      "url": "tutorials/multivariate/mds-metric.html",
      "topic": "multivariate",
      "tags": [
        "mds",
        "classical-mds",
        "pcoa",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Representing a distance matrix in low-dimensional Euclidean coordinates"
    },
    {
      "id": "multivariate/mds-nonmetric",
      "title": "Non-Metric Multidimensional Scaling",
      "url": "tutorials/multivariate/mds-nonmetric.html",
      "topic": "multivariate",
      "tags": [
        "nmds",
        "kruskal-stress",
        "ordinal",
        "topic:multivariate-methods",
        "hypothesis-testing",
        "non-parametric",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "MDS using only the ranks of distances, minimising Kruskal stress"
    },
    {
      "id": "multivariate/multiple-correspondence-analysis",
      "title": "Multiple Correspondence Analysis",
      "url": "tutorials/multivariate/multiple-correspondence-analysis.html",
      "topic": "multivariate",
      "tags": [
        "mca",
        "multiple-correspondence",
        "categorical",
        "topic:multivariate-methods",
        "categorical-data",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Extension of correspondence analysis to more than two categorical variables"
    },
    {
      "id": "multivariate/pam-kmedoids",
      "title": "Partitioning Around Medoids",
      "url": "tutorials/multivariate/pam-kmedoids.html",
      "topic": "multivariate",
      "tags": [
        "pam",
        "k-medoids",
        "robust",
        "gower",
        "topic:multivariate-methods",
        "multivariate-analysis",
        "robust-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "PAM: robust k-medoids clustering for non-Euclidean distances and outlier-contaminated data"
    },
    {
      "id": "multivariate/pls-da",
      "title": "PLS Discriminant Analysis",
      "url": "tutorials/multivariate/pls-da.html",
      "topic": "multivariate",
      "tags": [
        "plsda",
        "classification",
        "mixomics",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "PLS extended to classification via dummy-coded class indicators"
    },
    {
      "id": "multivariate/pls-regression",
      "title": "Partial Least Squares Regression",
      "url": "tutorials/multivariate/pls-regression.html",
      "topic": "multivariate",
      "tags": [
        "pls",
        "regression",
        "latent-components",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Regression using latent components optimised for outcome prediction in high dimensions"
    },
    {
      "id": "multivariate/principal-component-analysis",
      "title": "Principal Component Analysis",
      "url": "tutorials/multivariate/principal-component-analysis.html",
      "topic": "multivariate",
      "tags": [
        "pca",
        "dimensionality-reduction",
        "biplot",
        "multivariate",
        "topic:multivariate-methods",
        "multivariate-analysis",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Dimensionality reduction by variance-maximising projection, with interpretation, scaling, and visualisation in R"
    },
    {
      "id": "multivariate/quadratic-discriminant-analysis",
      "title": "Quadratic Discriminant Analysis",
      "url": "tutorials/multivariate/quadratic-discriminant-analysis.html",
      "topic": "multivariate",
      "tags": [
        "qda",
        "classification",
        "quadratic",
        "topic:multivariate-methods",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "LDA with class-specific covariance matrices; quadratic decision boundaries"
    },
    {
      "id": "multivariate/structural-equation-models",
      "title": "Structural Equation Models",
      "url": "tutorials/multivariate/structural-equation-models.html",
      "topic": "multivariate",
      "tags": [
        "sem",
        "lavaan",
        "latent",
        "path-analysis",
        "topic:multivariate-methods",
        "regression",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Combining measurement (CFA) and structural (path) components for latent-variable regression"
    },
    {
      "id": "multivariate/umap-tsne-overview",
      "title": "UMAP and t-SNE: Overview",
      "url": "tutorials/multivariate/umap-tsne-overview.html",
      "topic": "multivariate",
      "tags": [
        "umap",
        "tsne",
        "embedding",
        "visualisation",
        "topic:multivariate-methods",
        "multivariate-analysis",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Modern non-linear dimensionality reduction for visualisation: UMAP, t-SNE, and their caveats"
    },
    {
      "id": "probability/bayes-theorem",
      "title": "Bayes' Theorem",
      "url": "tutorials/probability/bayes-theorem.html",
      "topic": "probability",
      "tags": [
        "bayes-theorem",
        "prior",
        "posterior",
        "diagnostic",
        "topic:probability-theory",
        "bayesian-methods",
        "diagnostic-accuracy",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Inverting conditional probabilities: from likelihood to posterior, with applications to diagnostic testing and Bayesian inference"
    },
    {
      "id": "probability/bernoulli-distribution",
      "title": "The Bernoulli Distribution",
      "url": "tutorials/probability/bernoulli-distribution.html",
      "topic": "probability",
      "tags": [
        "bernoulli",
        "binary",
        "trial",
        "topic:probability-theory",
        "trial-design",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The distribution of a single binary trial with success probability p"
    },
    {
      "id": "probability/beta-distribution",
      "title": "The Beta Distribution",
      "url": "tutorials/probability/beta-distribution.html",
      "topic": "probability",
      "tags": [
        "beta",
        "proportion",
        "conjugate-prior",
        "topic:probability-theory",
        "bayesian-methods",
        "categorical-data",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Distribution over [0, 1] for proportions and probabilities; conjugate prior for the binomial"
    },
    {
      "id": "probability/binomial-distribution",
      "title": "The Binomial Distribution",
      "url": "tutorials/probability/binomial-distribution.html",
      "topic": "probability",
      "tags": [
        "binomial",
        "count",
        "normal-approximation",
        "topic:probability-theory",
        "categorical-data",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The count of successes in n independent Bernoulli trials with common probability p"
    },
    {
      "id": "probability/chi-squared-distribution",
      "title": "The Chi-Squared Distribution",
      "url": "tutorials/probability/chi-squared-distribution.html",
      "topic": "probability",
      "tags": [
        "chi-squared",
        "df",
        "variance",
        "goodness-of-fit",
        "topic:probability-theory",
        "hypothesis-testing",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sum of squared independent standard normals; foundation of chi-squared tests and ANOVA"
    },
    {
      "id": "probability/conditional-distributions",
      "title": "Conditional Distributions",
      "url": "tutorials/probability/conditional-distributions.html",
      "topic": "probability",
      "tags": [
        "conditional-distribution",
        "conditional-expectation",
        "tower-property",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The distribution of one random variable given the value of another, and conditional expectations"
    },
    {
      "id": "probability/conditional-probability",
      "title": "Conditional Probability",
      "url": "tutorials/probability/conditional-probability.html",
      "topic": "probability",
      "tags": [
        "conditional-probability",
        "product-rule",
        "conditioning",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Probability of one event given another, the product rule, and how conditioning reshapes the sample space"
    },
    {
      "id": "probability/convolutions",
      "title": "Convolutions of Distributions",
      "url": "tutorials/probability/convolutions.html",
      "topic": "probability",
      "tags": [
        "convolution",
        "sum",
        "closure",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Distribution of a sum of independent random variables, computed via convolution integrals"
    },
    {
      "id": "probability/copulas-introduction",
      "title": "Copulas: An Introduction",
      "url": "tutorials/probability/copulas-introduction.html",
      "topic": "probability",
      "tags": [
        "copula",
        "sklar",
        "dependence-structure",
        "tail-dependence",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Separating marginal distributions from the dependence structure via Sklar's theorem"
    },
    {
      "id": "probability/correlation-coefficient",
      "title": "The Correlation Coefficient",
      "url": "tutorials/probability/correlation-coefficient.html",
      "topic": "probability",
      "tags": [
        "correlation",
        "pearson",
        "linear-association",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Standardised covariance bounded in [-1, 1], measuring the strength of linear association"
    },
    {
      "id": "probability/cumulative-distribution-function",
      "title": "The Cumulative Distribution Function",
      "url": "tutorials/probability/cumulative-distribution-function.html",
      "topic": "probability",
      "tags": [
        "cdf",
        "quantile",
        "ecdf",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "F(x) = P(X ≤ x): the universal descriptor of a random variable's distribution"
    },
    {
      "id": "probability/discrete-vs-continuous",
      "title": "Discrete vs Continuous Variables",
      "url": "tutorials/probability/discrete-vs-continuous.html",
      "topic": "probability",
      "tags": [
        "discrete",
        "continuous",
        "mixed",
        "support",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Two fundamental kinds of random variable and the mixed case in between"
    },
    {
      "id": "probability/expectation",
      "title": "Expectation of a Random Variable",
      "url": "tutorials/probability/expectation.html",
      "topic": "probability",
      "tags": [
        "expectation",
        "mean",
        "lotus",
        "linearity",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The probability-weighted average of a random variable's values, with its algebraic properties"
    },
    {
      "id": "probability/exponential-distribution",
      "title": "The Exponential Distribution",
      "url": "tutorials/probability/exponential-distribution.html",
      "topic": "probability",
      "tags": [
        "exponential",
        "waiting-time",
        "memoryless",
        "hazard",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Waiting time between events in a Poisson process, with the memoryless property"
    },
    {
      "id": "probability/f-distribution",
      "title": "The F Distribution",
      "url": "tutorials/probability/f-distribution.html",
      "topic": "probability",
      "tags": [
        "f-distribution",
        "anova",
        "variance-ratio",
        "topic:probability-theory",
        "hypothesis-testing",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Ratio of two independent scaled chi-squared random variables; foundation of ANOVA and variance tests"
    },
    {
      "id": "probability/gamma-distribution",
      "title": "The Gamma Distribution",
      "url": "tutorials/probability/gamma-distribution.html",
      "topic": "probability",
      "tags": [
        "gamma",
        "shape",
        "rate",
        "positive-continuous",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Flexible right-skewed distribution for positive continuous data; sum of independent exponentials"
    },
    {
      "id": "probability/geometric-distribution",
      "title": "The Geometric Distribution",
      "url": "tutorials/probability/geometric-distribution.html",
      "topic": "probability",
      "tags": [
        "geometric",
        "memoryless",
        "waiting-time",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Number of Bernoulli trials until the first success, with the memoryless property"
    },
    {
      "id": "probability/hazard-function",
      "title": "The Hazard Function",
      "url": "tutorials/probability/hazard-function.html",
      "topic": "probability",
      "tags": [
        "hazard",
        "survival",
        "instantaneous-rate",
        "topic:probability-theory",
        "time-to-event",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Instantaneous event rate given survival so far; central concept of survival analysis"
    },
    {
      "id": "probability/hypergeometric-distribution",
      "title": "The Hypergeometric Distribution",
      "url": "tutorials/probability/hypergeometric-distribution.html",
      "topic": "probability",
      "tags": [
        "hypergeometric",
        "without-replacement",
        "fisher-exact",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sampling without replacement from a finite population"
    },
    {
      "id": "probability/independence",
      "title": "Independence",
      "url": "tutorials/probability/independence.html",
      "topic": "probability",
      "tags": [
        "independence",
        "mutual-independence",
        "pairwise",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Events and variables that do not inform each other, and the distinction between pairwise and mutual independence"
    },
    {
      "id": "probability/joint-distributions",
      "title": "Joint Distributions",
      "url": "tutorials/probability/joint-distributions.html",
      "topic": "probability",
      "tags": [
        "joint-distribution",
        "bivariate",
        "dependence",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The distribution of a pair (or vector) of random variables, encoding their dependence structure"
    },
    {
      "id": "probability/kolmogorov-axioms",
      "title": "Kolmogorov's Axioms of Probability",
      "url": "tutorials/probability/kolmogorov-axioms.html",
      "topic": "probability",
      "tags": [
        "kolmogorov",
        "axioms",
        "probability-space",
        "topic:probability-theory",
        "non-parametric",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The three axioms that define a probability measure on a sigma-algebra of events"
    },
    {
      "id": "probability/law-of-total-probability",
      "title": "Law of Total Probability",
      "url": "tutorials/probability/law-of-total-probability.html",
      "topic": "probability",
      "tags": [
        "total-probability",
        "partition",
        "marginalisation",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Decomposing a marginal probability over a partition of the sample space"
    },
    {
      "id": "probability/lognormal-distribution",
      "title": "The Log-Normal Distribution",
      "url": "tutorials/probability/lognormal-distribution.html",
      "topic": "probability",
      "tags": [
        "lognormal",
        "multiplicative",
        "skewed",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Positive-valued distribution whose logarithm is normal; multiplicative processes and biomedical concentrations"
    },
    {
      "id": "probability/marginal-distributions",
      "title": "Marginal Distributions",
      "url": "tutorials/probability/marginal-distributions.html",
      "topic": "probability",
      "tags": [
        "marginal",
        "integration",
        "joint",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Recovering the distribution of a single variable from a joint distribution by integrating out the others"
    },
    {
      "id": "probability/multinomial-distribution",
      "title": "The Multinomial Distribution",
      "url": "tutorials/probability/multinomial-distribution.html",
      "topic": "probability",
      "tags": [
        "multinomial",
        "categorical",
        "multi-class",
        "topic:probability-theory",
        "categorical-data",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Multi-category generalisation of the binomial with category probabilities summing to one"
    },
    {
      "id": "probability/multivariate-normal",
      "title": "The Multivariate Normal Distribution",
      "url": "tutorials/probability/multivariate-normal.html",
      "topic": "probability",
      "tags": [
        "multivariate-normal",
        "mvn",
        "covariance",
        "mahalanobis",
        "topic:probability-theory",
        "multivariate-analysis",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Generalisation of the normal to random vectors; the keystone of multivariate analysis"
    },
    {
      "id": "probability/negative-binomial-distribution",
      "title": "The Negative Binomial Distribution",
      "url": "tutorials/probability/negative-binomial-distribution.html",
      "topic": "probability",
      "tags": [
        "negative-binomial",
        "overdispersion",
        "count-data",
        "topic:probability-theory",
        "regression",
        "categorical-data",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Overdispersed count distribution, derivable as a gamma mixture of Poissons"
    },
    {
      "id": "probability/normal-distribution",
      "title": "The Normal Distribution",
      "url": "tutorials/probability/normal-distribution.html",
      "topic": "probability",
      "tags": [
        "normal-distribution",
        "gaussian",
        "probability",
        "distributions",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Definition, properties, and practical use of the normal (Gaussian) distribution, with R examples for every common task"
    },
    {
      "id": "probability/poisson-distribution",
      "title": "The Poisson Distribution",
      "url": "tutorials/probability/poisson-distribution.html",
      "topic": "probability",
      "tags": [
        "poisson",
        "count-data",
        "rate",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Counts of rare events per unit time or space with rate λ"
    },
    {
      "id": "probability/probability-density-function",
      "title": "The Probability Density Function",
      "url": "tutorials/probability/probability-density-function.html",
      "topic": "probability",
      "tags": [
        "pdf",
        "continuous",
        "density",
        "integration",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The function whose integral gives probability for a continuous random variable"
    },
    {
      "id": "probability/probability-mass-function",
      "title": "The Probability Mass Function",
      "url": "tutorials/probability/probability-mass-function.html",
      "topic": "probability",
      "tags": [
        "pmf",
        "discrete",
        "distribution",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The function that assigns probability to each value of a discrete random variable"
    },
    {
      "id": "probability/random-variables",
      "title": "Random Variables",
      "url": "tutorials/probability/random-variables.html",
      "topic": "probability",
      "tags": [
        "random-variable",
        "measurable-function",
        "distribution",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Measurable functions from sample space to real numbers: the mathematical object behind every statistic"
    },
    {
      "id": "probability/sample-space-events",
      "title": "Sample Space and Events",
      "url": "tutorials/probability/sample-space-events.html",
      "topic": "probability",
      "tags": [
        "sample-space",
        "events",
        "set-operations",
        "de-morgan",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The set of possible outcomes, events as subsets, and the algebra of set operations"
    },
    {
      "id": "probability/survival-function",
      "title": "The Survival Function",
      "url": "tutorials/probability/survival-function.html",
      "topic": "probability",
      "tags": [
        "survival-function",
        "complement-cdf",
        "kaplan-meier",
        "topic:probability-theory",
        "time-to-event",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Probability of surviving past time t; the complement of the CDF for positive random variables"
    },
    {
      "id": "probability/t-distribution",
      "title": "Student's t-Distribution",
      "url": "tutorials/probability/t-distribution.html",
      "topic": "probability",
      "tags": [
        "t-distribution",
        "degrees-of-freedom",
        "small-sample",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Heavier-tailed relative of the normal, used for small-sample inference on means"
    },
    {
      "id": "probability/transformations-of-rv",
      "title": "Transformations of Random Variables",
      "url": "tutorials/probability/transformations-of-rv.html",
      "topic": "probability",
      "tags": [
        "transformation",
        "change-of-variables",
        "jacobian",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Distribution of g(X) given the distribution of X, via the change-of-variables formula"
    },
    {
      "id": "probability/uniform-distribution",
      "title": "The Uniform Distribution",
      "url": "tutorials/probability/uniform-distribution.html",
      "topic": "probability",
      "tags": [
        "uniform",
        "continuous",
        "discrete",
        "simulation",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Equal probability over an interval (continuous) or a set (discrete)"
    },
    {
      "id": "probability/variance-covariance-of-rv",
      "title": "Variance and Covariance",
      "url": "tutorials/probability/variance-covariance-of-rv.html",
      "topic": "probability",
      "tags": [
        "variance",
        "covariance",
        "second-moment",
        "topic:probability-theory",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Second moments of one and two random variables: dispersion and joint variation"
    },
    {
      "id": "probability/weibull-distribution",
      "title": "The Weibull Distribution",
      "url": "tutorials/probability/weibull-distribution.html",
      "topic": "probability",
      "tags": [
        "weibull",
        "reliability",
        "survival",
        "hazard",
        "topic:probability-theory",
        "time-to-event",
        "agreement-and-reliability",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Flexible positive-valued distribution with monotone hazard; reliability and survival analysis"
    },
    {
      "id": "regression-modelling/added-variable-plots",
      "title": "Added-Variable Plots",
      "url": "tutorials/regression-modelling/added-variable-plots.html",
      "topic": "regression-modelling",
      "tags": [
        "added-variable-plot",
        "partial-regression",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Partial regression plots showing each predictor's unique contribution"
    },
    {
      "id": "regression-modelling/best-subset-selection",
      "title": "Best Subset Selection",
      "url": "tutorials/regression-modelling/best-subset-selection.html",
      "topic": "regression-modelling",
      "tags": [
        "best-subset",
        "leaps",
        "selection",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Enumerate all candidate predictor subsets to find the best by a chosen criterion"
    },
    {
      "id": "regression-modelling/beta-regression",
      "title": "Beta Regression",
      "url": "tutorials/regression-modelling/beta-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "beta-regression",
        "proportion",
        "betareg",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Regression for continuous outcomes in (0, 1): proportions, percentages, fractions"
    },
    {
      "id": "regression-modelling/centered-scaled-predictors",
      "title": "Centring and Scaling Predictors",
      "url": "tutorials/regression-modelling/centered-scaled-predictors.html",
      "topic": "regression-modelling",
      "tags": [
        "centring",
        "scaling",
        "interpretability",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Why and how to centre and scale continuous predictors before regression"
    },
    {
      "id": "regression-modelling/contrasts-in-r",
      "title": "Contrasts in R",
      "url": "tutorials/regression-modelling/contrasts-in-r.html",
      "topic": "regression-modelling",
      "tags": [
        "contrasts",
        "factor",
        "orthogonal",
        "helmert",
        "topic:regression-modelling",
        "regression",
        "hypothesis-testing"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Built-in and custom contrast matrices for factor variables in regression and ANOVA"
    },
    {
      "id": "regression-modelling/cooks-distance",
      "title": "Cook's Distance",
      "url": "tutorials/regression-modelling/cooks-distance.html",
      "topic": "regression-modelling",
      "tags": [
        "cooks-distance",
        "influence",
        "diagnostic",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Single-number influence measure combining leverage and residual"
    },
    {
      "id": "regression-modelling/dirichlet-regression",
      "title": "Dirichlet Regression",
      "url": "tutorials/regression-modelling/dirichlet-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "dirichlet",
        "compositional",
        "multivariate-proportion",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Regression for compositional outcomes: vectors of proportions summing to 1"
    },
    {
      "id": "regression-modelling/dummy-coding",
      "title": "Dummy Coding",
      "url": "tutorials/regression-modelling/dummy-coding.html",
      "topic": "regression-modelling",
      "tags": [
        "dummy-coding",
        "treatment-contrast",
        "reference-level",
        "topic:regression-modelling",
        "regression",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Treatment-contrast coding for categorical predictors: reference category and indicator variables"
    },
    {
      "id": "regression-modelling/effect-coding",
      "title": "Effect (Deviation) Coding",
      "url": "tutorials/regression-modelling/effect-coding.html",
      "topic": "regression-modelling",
      "tags": [
        "effect-coding",
        "deviation-contrast",
        "sum-to-zero",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sum-to-zero contrasts: coefficients as deviations from the grand mean"
    },
    {
      "id": "regression-modelling/elastic-net",
      "title": "Elastic Net",
      "url": "tutorials/regression-modelling/elastic-net.html",
      "topic": "regression-modelling",
      "tags": [
        "elastic-net",
        "l1-l2",
        "alpha",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Combined L1 and L2 penalty: variable selection plus grouping behaviour"
    },
    {
      "id": "regression-modelling/gam-introduction",
      "title": "GAMs: Introduction",
      "url": "tutorials/regression-modelling/gam-introduction.html",
      "topic": "regression-modelling",
      "tags": [
        "gam",
        "smooth",
        "mgcv",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Generalised additive models with smoothing splines for non-linear predictor effects"
    },
    {
      "id": "regression-modelling/gamm-additive-mixed",
      "title": "GAMMs: Additive Mixed Models",
      "url": "tutorials/regression-modelling/gamm-additive-mixed.html",
      "topic": "regression-modelling",
      "tags": [
        "gamm",
        "additive",
        "smooth",
        "mixed",
        "topic:regression-modelling",
        "regression",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Combining smooth effects (GAM) with random effects (LMM) via mgcv::gam"
    },
    {
      "id": "regression-modelling/generalized-estimating-equations",
      "title": "Generalised Estimating Equations",
      "url": "tutorials/regression-modelling/generalized-estimating-equations.html",
      "topic": "regression-modelling",
      "tags": [
        "gee",
        "marginal-model",
        "working-correlation",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Marginal regression models for clustered data with working correlation structures"
    },
    {
      "id": "regression-modelling/glmer-generalized-mixed",
      "title": "glmer: Generalised Mixed Models",
      "url": "tutorials/regression-modelling/glmer-generalized-mixed.html",
      "topic": "regression-modelling",
      "tags": [
        "glmer",
        "glmm",
        "binary-mixed",
        "count-mixed",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Extending mixed models to binary, count, and other GLM outcomes"
    },
    {
      "id": "regression-modelling/hurdle-models",
      "title": "Hurdle Models",
      "url": "tutorials/regression-modelling/hurdle-models.html",
      "topic": "regression-modelling",
      "tags": [
        "hurdle",
        "truncated",
        "two-part",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Two-part count models: binary decision for zero vs. non-zero, plus truncated count for positives"
    },
    {
      "id": "regression-modelling/interactions-in-regression",
      "title": "Interactions in Regression",
      "url": "tutorials/regression-modelling/interactions-in-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "interaction",
        "product-term",
        "moderator",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Product terms in regression, centring, and the interpretation of conditional effects"
    },
    {
      "id": "regression-modelling/lasso-regression",
      "title": "Lasso Regression",
      "url": "tutorials/regression-modelling/lasso-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "lasso",
        "l1-penalty",
        "variable-selection",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "L1-penalised regression for simultaneous shrinkage and variable selection"
    },
    {
      "id": "regression-modelling/leverage-influence",
      "title": "Leverage and Influence",
      "url": "tutorials/regression-modelling/leverage-influence.html",
      "topic": "regression-modelling",
      "tags": [
        "leverage",
        "influence",
        "hat-values",
        "dfbeta",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Hat values, DFBETAs, and Cook's distance for identifying influential observations"
    },
    {
      "id": "regression-modelling/lme4-lmer-examples",
      "title": "Worked lme4::lmer Examples",
      "url": "tutorials/regression-modelling/lme4-lmer-examples.html",
      "topic": "regression-modelling",
      "tags": [
        "lme4",
        "lmer",
        "formula-syntax",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Common formula patterns for lmer with interpretation of each output block"
    },
    {
      "id": "regression-modelling/logistic-regression-diagnostics",
      "title": "Logistic Regression Diagnostics",
      "url": "tutorials/regression-modelling/logistic-regression-diagnostics.html",
      "topic": "regression-modelling",
      "tags": [
        "logistic-diagnostics",
        "calibration",
        "hosmer-lemeshow",
        "topic:regression-modelling",
        "regression",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Calibration plots, Hosmer-Lemeshow, deviance residuals, and influence for GLM"
    },
    {
      "id": "regression-modelling/logistic-regression",
      "title": "Logistic Regression",
      "url": "tutorials/regression-modelling/logistic-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "logistic",
        "binary-outcome",
        "logit",
        "odds-ratio",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Regression for binary outcomes: odds ratios, logit link, and MLE fitting"
    },
    {
      "id": "regression-modelling/mixed-models-intro",
      "title": "Mixed-Effects Models: Introduction",
      "url": "tutorials/regression-modelling/mixed-models-intro.html",
      "topic": "regression-modelling",
      "tags": [
        "mixed-effects",
        "multilevel",
        "random-effects",
        "lmer",
        "topic:regression-modelling",
        "regression",
        "longitudinal-and-mixed-models",
        "multivariate-analysis",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Hierarchical / multilevel regression: fixed effects for population-level structure, random effects for cluster-level variability"
    },
    {
      "id": "regression-modelling/model-selection-aic-bic",
      "title": "Model Selection with AIC and BIC",
      "url": "tutorials/regression-modelling/model-selection-aic-bic.html",
      "topic": "regression-modelling",
      "tags": [
        "aic",
        "bic",
        "model-selection",
        "information-criteria",
        "topic:regression-modelling",
        "regression",
        "bayesian-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Information criteria for comparing models: Akaike's and Bayesian"
    },
    {
      "id": "regression-modelling/multicollinearity-vif",
      "title": "Multicollinearity and VIF",
      "url": "tutorials/regression-modelling/multicollinearity-vif.html",
      "topic": "regression-modelling",
      "tags": [
        "multicollinearity",
        "vif",
        "condition-number",
        "ridge",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Variance inflation factors, condition numbers, and remedies for collinear predictors"
    },
    {
      "id": "regression-modelling/multinomial-logistic",
      "title": "Multinomial Logistic Regression",
      "url": "tutorials/regression-modelling/multinomial-logistic.html",
      "topic": "regression-modelling",
      "tags": [
        "multinomial",
        "categorical-outcome",
        "softmax",
        "topic:regression-modelling",
        "regression",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Regression for unordered categorical outcomes with three or more levels"
    },
    {
      "id": "regression-modelling/multiple-linear-regression",
      "title": "Multiple Linear Regression",
      "url": "tutorials/regression-modelling/multiple-linear-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "multiple-regression",
        "ols",
        "collinearity",
        "partial-effect",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Linear regression with two or more predictors: coefficient interpretation, partial effects, and collinearity"
    },
    {
      "id": "regression-modelling/negative-binomial-regression",
      "title": "Negative Binomial Regression",
      "url": "tutorials/regression-modelling/negative-binomial-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "negative-binomial",
        "nb",
        "overdispersion",
        "topic:regression-modelling",
        "regression",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Count regression with explicit variance-dispersion parameter for overdispersed data"
    },
    {
      "id": "regression-modelling/nested-crossed-random-effects",
      "title": "Nested vs Crossed Random Effects",
      "url": "tutorials/regression-modelling/nested-crossed-random-effects.html",
      "topic": "regression-modelling",
      "tags": [
        "nested",
        "crossed",
        "random-effects",
        "hierarchy",
        "topic:regression-modelling",
        "regression",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Distinguishing hierarchical from partially-overlapping grouping structures in mixed models"
    },
    {
      "id": "regression-modelling/offsets-in-regression",
      "title": "Offsets in Regression",
      "url": "tutorials/regression-modelling/offsets-in-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "offset",
        "rate",
        "exposure",
        "person-years",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Treating exposure time or person-years as a known component of the linear predictor"
    },
    {
      "id": "regression-modelling/ordinal-logistic",
      "title": "Ordinal Logistic Regression",
      "url": "tutorials/regression-modelling/ordinal-logistic.html",
      "topic": "regression-modelling",
      "tags": [
        "ordinal",
        "proportional-odds",
        "polr",
        "topic:regression-modelling",
        "regression",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Regression for ordered categorical outcomes using the proportional-odds model"
    },
    {
      "id": "regression-modelling/partial-correlation",
      "title": "Partial Correlation",
      "url": "tutorials/regression-modelling/partial-correlation.html",
      "topic": "regression-modelling",
      "tags": [
        "partial-correlation",
        "confounding",
        "adjustment",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Correlation between two variables after removing the linear influence of others"
    },
    {
      "id": "regression-modelling/poisson-regression",
      "title": "Poisson Regression",
      "url": "tutorials/regression-modelling/poisson-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "poisson",
        "count-regression",
        "offset",
        "rate",
        "topic:regression-modelling",
        "regression",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Log-linear count regression with rates and offsets"
    },
    {
      "id": "regression-modelling/polynomial-regression",
      "title": "Polynomial Regression",
      "url": "tutorials/regression-modelling/polynomial-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "polynomial",
        "orthogonal",
        "overfitting",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Extending linear regression with powers of predictors: orthogonal polynomials and overfitting risk"
    },
    {
      "id": "regression-modelling/probit-regression",
      "title": "Probit Regression",
      "url": "tutorials/regression-modelling/probit-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "probit",
        "binary",
        "glm",
        "topic:regression-modelling",
        "regression",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Binary regression using the cumulative normal link"
    },
    {
      "id": "regression-modelling/quantile-regression",
      "title": "Quantile Regression",
      "url": "tutorials/regression-modelling/quantile-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "quantile",
        "median-regression",
        "rq",
        "topic:regression-modelling",
        "regression",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Modelling conditional quantiles (median, upper/lower percentiles) rather than the mean"
    },
    {
      "id": "regression-modelling/quasi-poisson",
      "title": "Quasi-Poisson Regression",
      "url": "tutorials/regression-modelling/quasi-poisson.html",
      "topic": "regression-modelling",
      "tags": [
        "quasi-poisson",
        "overdispersion",
        "dispersion",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Poisson regression with a dispersion parameter for moderate overdispersion"
    },
    {
      "id": "regression-modelling/r-squared-adjusted",
      "title": "R-Squared and Adjusted R-Squared",
      "url": "tutorials/regression-modelling/r-squared-adjusted.html",
      "topic": "regression-modelling",
      "tags": [
        "r-squared",
        "adjusted-r-squared",
        "variance-explained",
        "topic:regression-modelling",
        "regression",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Proportion of variance explained, and the penalty for model complexity"
    },
    {
      "id": "regression-modelling/random-intercepts",
      "title": "Random-Intercept Models",
      "url": "tutorials/regression-modelling/random-intercepts.html",
      "topic": "regression-modelling",
      "tags": [
        "random-intercept",
        "mixed-effects",
        "icc",
        "topic:regression-modelling",
        "regression",
        "longitudinal-and-mixed-models",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Mixed model with cluster-specific intercepts but common slopes"
    },
    {
      "id": "regression-modelling/random-slopes",
      "title": "Random-Slope Models",
      "url": "tutorials/regression-modelling/random-slopes.html",
      "topic": "regression-modelling",
      "tags": [
        "random-slope",
        "mixed-effects",
        "heterogeneity",
        "topic:regression-modelling",
        "regression",
        "longitudinal-and-mixed-models",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Mixed model with cluster-specific slopes, allowing effects to vary across groups"
    },
    {
      "id": "regression-modelling/regression-assumptions",
      "title": "Linear Regression Assumptions",
      "url": "tutorials/regression-modelling/regression-assumptions.html",
      "topic": "regression-modelling",
      "tags": [
        "assumptions",
        "linearity",
        "homoscedasticity",
        "normality",
        "independence",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The four classical assumptions of linear regression and how to check each"
    },
    {
      "id": "regression-modelling/residual-diagnostics",
      "title": "Residual Diagnostics",
      "url": "tutorials/regression-modelling/residual-diagnostics.html",
      "topic": "regression-modelling",
      "tags": [
        "residuals",
        "diagnostics",
        "qq-plot",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Four standard residual plots, what they show, and how to read them"
    },
    {
      "id": "regression-modelling/ridge-regression",
      "title": "Ridge Regression",
      "url": "tutorials/regression-modelling/ridge-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "ridge",
        "l2-penalty",
        "shrinkage",
        "glmnet",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "L2 penalty for coefficient shrinkage; stable fits under collinearity"
    },
    {
      "id": "regression-modelling/robust-regression",
      "title": "Robust Regression",
      "url": "tutorials/regression-modelling/robust-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "robust",
        "m-estimator",
        "huber",
        "mm-estimator",
        "topic:regression-modelling",
        "regression",
        "robust-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "M-estimators and MM-estimators for regression resistant to outliers and heavy-tailed errors"
    },
    {
      "id": "regression-modelling/roc-auc-for-logistic",
      "title": "ROC and AUC for Logistic Models",
      "url": "tutorials/regression-modelling/roc-auc-for-logistic.html",
      "topic": "regression-modelling",
      "tags": [
        "roc",
        "auc",
        "logistic",
        "discrimination",
        "topic:regression-modelling",
        "regression",
        "diagnostic-accuracy"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Discrimination quality of logistic regression via receiver operating characteristic curves"
    },
    {
      "id": "regression-modelling/simple-linear-regression",
      "title": "Simple Linear Regression",
      "url": "tutorials/regression-modelling/simple-linear-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "linear-regression",
        "ols",
        "residuals",
        "diagnostics",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Fitting, diagnosing, and interpreting a linear regression with a single continuous predictor, from first principles"
    },
    {
      "id": "regression-modelling/spline-regression",
      "title": "Spline Regression",
      "url": "tutorials/regression-modelling/spline-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "spline",
        "natural-cubic-spline",
        "knots",
        "topic:regression-modelling",
        "regression"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Piecewise-polynomial regression with natural cubic splines for flexible non-linear fits"
    },
    {
      "id": "regression-modelling/stepwise-regression-pitfalls",
      "title": "Stepwise Regression: Pitfalls",
      "url": "tutorials/regression-modelling/stepwise-regression-pitfalls.html",
      "topic": "regression-modelling",
      "tags": [
        "stepwise",
        "selection",
        "p-hacking",
        "topic:regression-modelling",
        "regression",
        "hypothesis-testing"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Why automated predictor selection by p-value or AIC has serious statistical problems"
    },
    {
      "id": "regression-modelling/survival-regression-cox",
      "title": "Cox Regression as Regression",
      "url": "tutorials/regression-modelling/survival-regression-cox.html",
      "topic": "regression-modelling",
      "tags": [
        "cox",
        "proportional-hazards",
        "survival-regression",
        "topic:regression-modelling",
        "regression",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The Cox proportional-hazards model in the GLM-family perspective; pointer to the Survival section"
    },
    {
      "id": "regression-modelling/tobit-regression",
      "title": "Tobit Regression",
      "url": "tutorials/regression-modelling/tobit-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "tobit",
        "censored",
        "latent-variable",
        "topic:regression-modelling",
        "regression",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Linear regression for outcomes censored at a known threshold"
    },
    {
      "id": "regression-modelling/truncated-regression",
      "title": "Truncated Regression",
      "url": "tutorials/regression-modelling/truncated-regression.html",
      "topic": "regression-modelling",
      "tags": [
        "truncation",
        "truncated-normal",
        "sample-selection",
        "topic:regression-modelling",
        "regression",
        "time-to-event",
        "missing-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Regression when observations are missing (not just censored) below or above a threshold"
    },
    {
      "id": "regression-modelling/zero-inflated-models",
      "title": "Zero-Inflated Models",
      "url": "tutorials/regression-modelling/zero-inflated-models.html",
      "topic": "regression-modelling",
      "tags": [
        "zero-inflation",
        "zip",
        "zinb",
        "mixture",
        "topic:regression-modelling",
        "regression",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Mixture models for count data with more zeros than Poisson or negative binomial predicts"
    },
    {
      "id": "sample-size/effect-size-cohens-d",
      "title": "Effect Size: Cohen's d",
      "url": "tutorials/sample-size/effect-size-cohens-d.html",
      "topic": "sample-size",
      "tags": [
        "effect-size",
        "cohen-d",
        "standardised-difference",
        "topic:sample-size-power",
        "power-and-sample-size"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Standardised mean difference: definition, variants, interpretation"
    },
    {
      "id": "sample-size/effect-size-cohens-h",
      "title": "Effect Size: Cohen's h",
      "url": "tutorials/sample-size/effect-size-cohens-h.html",
      "topic": "sample-size",
      "tags": [
        "effect-size",
        "cohen-h",
        "proportions",
        "arcsine",
        "topic:sample-size-power",
        "power-and-sample-size"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Standardised effect size for comparisons between proportions via arcsine transformation"
    },
    {
      "id": "sample-size/effect-size-eta-squared",
      "title": "Effect Size: Eta-Squared",
      "url": "tutorials/sample-size/effect-size-eta-squared.html",
      "topic": "sample-size",
      "tags": [
        "effect-size",
        "eta-squared",
        "anova",
        "variance-explained",
        "topic:sample-size-power",
        "hypothesis-testing",
        "power-and-sample-size",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Proportion of variance explained in ANOVA and its partial, generalised, and omega variants"
    },
    {
      "id": "sample-size/minimum-detectable-effect",
      "title": "Minimum Detectable Effect",
      "url": "tutorials/sample-size/minimum-detectable-effect.html",
      "topic": "sample-size",
      "tags": [
        "mde",
        "detectable-effect",
        "power-analysis",
        "topic:sample-size-power",
        "power-and-sample-size"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Solving for the smallest effect that can be detected at a given sample size and power"
    },
    {
      "id": "sample-size/post-hoc-power-controversy",
      "title": "Post-Hoc Power: A Controversy",
      "url": "tutorials/sample-size/post-hoc-power-controversy.html",
      "topic": "sample-size",
      "tags": [
        "post-hoc-power",
        "observed-power",
        "critique",
        "topic:sample-size-power",
        "power-and-sample-size"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Why computing power from the observed effect is circular and uninformative"
    },
    {
      "id": "sample-size/power-agreement-kappa",
      "title": "Power for Agreement (Kappa)",
      "url": "tutorials/sample-size/power-agreement-kappa.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "kappa",
        "agreement",
        "reliability",
        "topic:sample-size-power",
        "hypothesis-testing",
        "effect-size",
        "power-and-sample-size",
        "agreement-and-reliability"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sample size for Cohen's kappa as a precision-targeted or hypothesis-test planning quantity"
    },
    {
      "id": "sample-size/power-analysis-introduction",
      "title": "Power Analysis: Introduction",
      "url": "tutorials/sample-size/power-analysis-introduction.html",
      "topic": "sample-size",
      "tags": [
        "power-analysis",
        "sample-size",
        "effect-size",
        "alpha",
        "topic:sample-size-power",
        "power-and-sample-size"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The four quantities of power analysis and how they trade against each other in study design"
    },
    {
      "id": "sample-size/power-anova",
      "title": "Power for One-Way ANOVA",
      "url": "tutorials/sample-size/power-anova.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "anova",
        "cohen-f",
        "non-central-f",
        "topic:sample-size-power",
        "hypothesis-testing",
        "power-and-sample-size"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sample size for detecting a difference among three or more group means"
    },
    {
      "id": "sample-size/power-bland-altman",
      "title": "Power for Bland-Altman Studies",
      "url": "tutorials/sample-size/power-bland-altman.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "bland-altman",
        "limits-of-agreement",
        "method-comparison",
        "topic:sample-size-power",
        "power-and-sample-size",
        "agreement-and-reliability"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sample size for estimating limits of agreement with adequate precision"
    },
    {
      "id": "sample-size/power-chi-squared",
      "title": "Power for Chi-Squared Tests",
      "url": "tutorials/sample-size/power-chi-squared.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "chi-squared",
        "cohen-w",
        "contingency",
        "topic:sample-size-power",
        "hypothesis-testing",
        "effect-size",
        "power-and-sample-size",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sample size for goodness-of-fit and contingency chi-squared using Cohen's w"
    },
    {
      "id": "sample-size/power-cluster-rct",
      "title": "Power for Cluster-RCT",
      "url": "tutorials/sample-size/power-cluster-rct.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "cluster-rct",
        "icc",
        "design-effect",
        "topic:sample-size-power",
        "power-and-sample-size",
        "agreement-and-reliability",
        "multivariate-analysis",
        "experimental-design",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sample size for cluster-randomised trials: design effect from ICC and cluster size"
    },
    {
      "id": "sample-size/power-correlation",
      "title": "Power for Correlation Tests",
      "url": "tutorials/sample-size/power-correlation.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "correlation",
        "fisher-z",
        "topic:sample-size-power",
        "hypothesis-testing",
        "non-parametric",
        "power-and-sample-size",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sample size for Pearson and Spearman correlations via Fisher's z transformation"
    },
    {
      "id": "sample-size/power-cox-regression",
      "title": "Power for Cox Regression",
      "url": "tutorials/sample-size/power-cox-regression.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "cox-regression",
        "events",
        "hazard-ratio",
        "topic:sample-size-power",
        "regression",
        "power-and-sample-size",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Events-driven sample size: the number of events, not subjects, drives power"
    },
    {
      "id": "sample-size/power-crossover",
      "title": "Power for Crossover Trials",
      "url": "tutorials/sample-size/power-crossover.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "crossover",
        "2x2",
        "carryover",
        "topic:sample-size-power",
        "power-and-sample-size",
        "experimental-design",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sample size for 2x2 crossover designs, exploiting within-subject comparison"
    },
    {
      "id": "sample-size/power-diagnostic-accuracy",
      "title": "Power for Diagnostic Accuracy",
      "url": "tutorials/sample-size/power-diagnostic-accuracy.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "diagnostic-accuracy",
        "sensitivity",
        "specificity",
        "topic:sample-size-power",
        "power-and-sample-size"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sample size for estimating sensitivity and specificity with adequate precision"
    },
    {
      "id": "sample-size/power-equivalence-tost",
      "title": "Power for Equivalence (TOST)",
      "url": "tutorials/sample-size/power-equivalence-tost.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "tost",
        "equivalence",
        "margin",
        "topic:sample-size-power",
        "power-and-sample-size",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sample size for two-one-sided-tests equivalence testing within a pre-specified margin"
    },
    {
      "id": "sample-size/power-icc",
      "title": "Power for ICC",
      "url": "tutorials/sample-size/power-icc.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "icc",
        "reliability",
        "continuous-rating",
        "topic:sample-size-power",
        "power-and-sample-size",
        "agreement-and-reliability"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sample size for the intraclass correlation coefficient in reliability studies"
    },
    {
      "id": "sample-size/power-linear-regression",
      "title": "Power for Linear Regression",
      "url": "tutorials/sample-size/power-linear-regression.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "linear-regression",
        "f-squared",
        "r-squared",
        "topic:sample-size-power",
        "regression",
        "effect-size",
        "power-and-sample-size"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sample size for overall regression F and for individual coefficients via Cohen's f^2"
    },
    {
      "id": "sample-size/power-logistic-regression",
      "title": "Power for Logistic Regression",
      "url": "tutorials/sample-size/power-logistic-regression.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "logistic-regression",
        "odds-ratio",
        "epv",
        "topic:sample-size-power",
        "regression",
        "power-and-sample-size"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sample size for logistic regression: events per variable, odds-ratio detection, and simulation"
    },
    {
      "id": "sample-size/power-logrank-test",
      "title": "Power for the Log-Rank Test",
      "url": "tutorials/sample-size/power-logrank-test.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "log-rank",
        "events",
        "accrual",
        "topic:sample-size-power",
        "power-and-sample-size",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Event-based sample size for Kaplan-Meier comparisons via the log-rank test"
    },
    {
      "id": "sample-size/power-mcnemar",
      "title": "Power for McNemar's Test",
      "url": "tutorials/sample-size/power-mcnemar.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "mcnemar",
        "paired-binary",
        "discordant",
        "topic:sample-size-power",
        "hypothesis-testing",
        "power-and-sample-size",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sample size for paired binary comparisons driven by the rate of discordant pairs"
    },
    {
      "id": "sample-size/power-non-inferiority",
      "title": "Power for Non-Inferiority Trials",
      "url": "tutorials/sample-size/power-non-inferiority.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "non-inferiority",
        "margin",
        "one-sided",
        "topic:sample-size-power",
        "power-and-sample-size",
        "trial-design"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "One-sided equivalence: is the new treatment no worse than the reference by more than the margin"
    },
    {
      "id": "sample-size/power-one-proportion",
      "title": "Power for One-Proportion Test",
      "url": "tutorials/sample-size/power-one-proportion.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "proportion",
        "one-sample",
        "exact",
        "topic:sample-size-power",
        "power-and-sample-size",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sample size for testing a single proportion against a reference, with normal and exact methods"
    },
    {
      "id": "sample-size/power-one-sample-t",
      "title": "Power for One-Sample t-Test",
      "url": "tutorials/sample-size/power-one-sample-t.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "one-sample",
        "t-test",
        "cohen-d",
        "topic:sample-size-power",
        "hypothesis-testing",
        "power-and-sample-size"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Computing sample size and power for a test of one mean against a reference"
    },
    {
      "id": "sample-size/power-paired-t",
      "title": "Power for Paired t-Test",
      "url": "tutorials/sample-size/power-paired-t.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "paired-t-test",
        "correlation",
        "topic:sample-size-power",
        "hypothesis-testing",
        "power-and-sample-size"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sample size for paired designs, taking advantage of within-subject correlation"
    },
    {
      "id": "sample-size/power-repeated-measures",
      "title": "Power for Repeated-Measures ANOVA",
      "url": "tutorials/sample-size/power-repeated-measures.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "rmanova",
        "within-subject",
        "correlation",
        "topic:sample-size-power",
        "hypothesis-testing",
        "power-and-sample-size",
        "longitudinal-and-mixed-models"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sample size for within-subjects designs with within-subject correlation"
    },
    {
      "id": "sample-size/power-stepped-wedge",
      "title": "Power for Stepped-Wedge Trials",
      "url": "tutorials/sample-size/power-stepped-wedge.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "stepped-wedge",
        "hussey-hughes",
        "cluster",
        "topic:sample-size-power",
        "power-and-sample-size",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Variance of the treatment effect in stepped-wedge cluster designs via the Hussey-Hughes formula"
    },
    {
      "id": "sample-size/power-two-proportions",
      "title": "Power for Two-Proportion Test",
      "url": "tutorials/sample-size/power-two-proportions.html",
      "topic": "sample-size",
      "tags": [
        "power",
        "two-proportion",
        "cohen-h",
        "unequal-n",
        "topic:sample-size-power",
        "power-and-sample-size",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sample size for comparing proportions between two independent groups"
    },
    {
      "id": "sample-size/power-two-sample-t",
      "title": "Sample Size for a Two-Sample t-Test",
      "url": "tutorials/sample-size/power-two-sample-t.html",
      "topic": "sample-size",
      "tags": [
        "power-analysis",
        "sample-size",
        "t-test",
        "effect-size",
        "study-design",
        "topic:sample-size-power",
        "hypothesis-testing",
        "power-and-sample-size"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Power analysis and sample size calculation for comparing two independent group means, with worked examples in R"
    },
    {
      "id": "sample-size/sensitivity-analysis-sample-size",
      "title": "Sample Size Sensitivity Analysis",
      "url": "tutorials/sample-size/sensitivity-analysis-sample-size.html",
      "topic": "sample-size",
      "tags": [
        "sensitivity",
        "sample-size",
        "assumptions",
        "topic:sample-size-power",
        "power-and-sample-size",
        "diagnostic-accuracy"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Reporting sample size across a range of assumed effect sizes, SDs, and dropout rates"
    },
    {
      "id": "statistical-foundations/bias-and-variance",
      "title": "Bias and Variance of Estimators",
      "url": "tutorials/statistical-foundations/bias-and-variance.html",
      "topic": "statistical-foundations",
      "tags": [
        "bias",
        "variance",
        "mse",
        "estimator",
        "decomposition",
        "topic:statistical-foundations"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Decomposing an estimator's mean squared error into systematic bias and sampling variance"
    },
    {
      "id": "statistical-foundations/big-op-little-op",
      "title": "Big-Op and Little-op Notation",
      "url": "tutorials/statistical-foundations/big-op-little-op.html",
      "topic": "statistical-foundations",
      "tags": [
        "big-op",
        "little-op",
        "asymptotic",
        "order",
        "topic:statistical-foundations",
        "asymptotic-theory"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Stochastic order symbols for describing rates of convergence and stochastic magnitudes"
    },
    {
      "id": "statistical-foundations/cauchy-schwarz",
      "title": "The Cauchy-Schwarz Inequality",
      "url": "tutorials/statistical-foundations/cauchy-schwarz.html",
      "topic": "statistical-foundations",
      "tags": [
        "cauchy-schwarz",
        "correlation",
        "inequality",
        "topic:statistical-foundations"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The inner-product inequality that bounds correlations and drives many variance inequalities"
    },
    {
      "id": "statistical-foundations/central-limit-theorem",
      "title": "The Central Limit Theorem",
      "url": "tutorials/statistical-foundations/central-limit-theorem.html",
      "topic": "statistical-foundations",
      "tags": [
        "central-limit-theorem",
        "sampling-distribution",
        "normal-distribution",
        "inference",
        "topic:statistical-foundations",
        "probability-and-distributions",
        "asymptotic-theory"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Why the sampling distribution of the mean is approximately normal, and what that means for everyday inference"
    },
    {
      "id": "statistical-foundations/characteristic-functions",
      "title": "Characteristic Functions",
      "url": "tutorials/statistical-foundations/characteristic-functions.html",
      "topic": "statistical-foundations",
      "tags": [
        "characteristic-function",
        "fourier",
        "convergence",
        "topic:statistical-foundations",
        "probability-and-distributions",
        "asymptotic-theory"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Fourier transforms of distributions: always exist, uniquely identify the distribution, and drive convergence theorems"
    },
    {
      "id": "statistical-foundations/chebyshev-markov",
      "title": "Markov and Chebyshev Inequalities",
      "url": "tutorials/statistical-foundations/chebyshev-markov.html",
      "topic": "statistical-foundations",
      "tags": [
        "markov",
        "chebyshev",
        "tail-bound",
        "inequality",
        "topic:statistical-foundations"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Tail bounds derived from moments, with the LLN as a simple consequence"
    },
    {
      "id": "statistical-foundations/confidence-intervals-introduction",
      "title": "Confidence Intervals: Introduction",
      "url": "tutorials/statistical-foundations/confidence-intervals-introduction.html",
      "topic": "statistical-foundations",
      "tags": [
        "confidence-interval",
        "coverage",
        "interval-estimate",
        "topic:statistical-foundations"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Interval estimates with a coverage guarantee, and how they differ from credible intervals"
    },
    {
      "id": "statistical-foundations/convergence-in-distribution",
      "title": "Convergence in Distribution",
      "url": "tutorials/statistical-foundations/convergence-in-distribution.html",
      "topic": "statistical-foundations",
      "tags": [
        "convergence-in-distribution",
        "weak-convergence",
        "clt",
        "topic:statistical-foundations",
        "probability-and-distributions",
        "asymptotic-theory"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The weakest standard mode of convergence, underlying the central limit theorem"
    },
    {
      "id": "statistical-foundations/convergence-in-probability",
      "title": "Convergence in Probability",
      "url": "tutorials/statistical-foundations/convergence-in-probability.html",
      "topic": "statistical-foundations",
      "tags": [
        "convergence-in-probability",
        "consistency",
        "asymptotic",
        "topic:statistical-foundations",
        "probability-and-distributions",
        "asymptotic-theory"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The mode of convergence that underpins consistency of estimators"
    },
    {
      "id": "statistical-foundations/cramer-rao-lower-bound",
      "title": "The Cramer-Rao Lower Bound",
      "url": "tutorials/statistical-foundations/cramer-rao-lower-bound.html",
      "topic": "statistical-foundations",
      "tags": [
        "cramer-rao",
        "efficiency",
        "bound",
        "fisher-information",
        "topic:statistical-foundations",
        "hypothesis-testing",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The smallest variance an unbiased estimator can achieve, derived from Fisher information"
    },
    {
      "id": "statistical-foundations/delta-method",
      "title": "The Delta Method",
      "url": "tutorials/statistical-foundations/delta-method.html",
      "topic": "statistical-foundations",
      "tags": [
        "delta-method",
        "asymptotic",
        "variance",
        "transformation",
        "topic:statistical-foundations",
        "asymptotic-theory"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Asymptotic distributions of smooth functions of estimators, via a first-order Taylor expansion"
    },
    {
      "id": "statistical-foundations/empirical-distribution-function",
      "title": "The Empirical Distribution Function",
      "url": "tutorials/statistical-foundations/empirical-distribution-function.html",
      "topic": "statistical-foundations",
      "tags": [
        "ecdf",
        "cdf",
        "dkw",
        "empirical-distribution",
        "topic:statistical-foundations",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The sample-based step-function estimator of the CDF, with the DKW inequality for its uniform error"
    },
    {
      "id": "statistical-foundations/estimators-and-estimation",
      "title": "Estimators and Estimation",
      "url": "tutorials/statistical-foundations/estimators-and-estimation.html",
      "topic": "statistical-foundations",
      "tags": [
        "estimation",
        "estimator",
        "plug-in",
        "point-estimate",
        "topic:statistical-foundations"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Point estimation and the plug-in principle: turning a sample into a guess about a population parameter"
    },
    {
      "id": "statistical-foundations/fisher-information",
      "title": "Fisher Information",
      "url": "tutorials/statistical-foundations/fisher-information.html",
      "topic": "statistical-foundations",
      "tags": [
        "fisher-information",
        "score",
        "observed-information",
        "efficiency",
        "topic:statistical-foundations",
        "hypothesis-testing",
        "categorical-data"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "How much the data tell you about a parameter, measured by the curvature of the log-likelihood"
    },
    {
      "id": "statistical-foundations/glivenko-cantelli",
      "title": "The Glivenko-Cantelli Theorem",
      "url": "tutorials/statistical-foundations/glivenko-cantelli.html",
      "topic": "statistical-foundations",
      "tags": [
        "glivenko-cantelli",
        "ecdf",
        "uniform-convergence",
        "topic:statistical-foundations",
        "asymptotic-theory"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The uniform LLN for CDFs: the ECDF converges almost surely to the true CDF at every point simultaneously"
    },
    {
      "id": "statistical-foundations/jensens-inequality",
      "title": "Jensen's Inequality",
      "url": "tutorials/statistical-foundations/jensens-inequality.html",
      "topic": "statistical-foundations",
      "tags": [
        "jensen",
        "convex",
        "inequality",
        "expectation",
        "topic:statistical-foundations"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The expectation of a convex function is at least the convex function of the expectation"
    },
    {
      "id": "statistical-foundations/law-of-large-numbers",
      "title": "The Law of Large Numbers",
      "url": "tutorials/statistical-foundations/law-of-large-numbers.html",
      "topic": "statistical-foundations",
      "tags": [
        "law-of-large-numbers",
        "convergence",
        "consistency",
        "topic:statistical-foundations",
        "asymptotic-theory"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Why the sample mean converges to the population mean as the sample grows, in weak and strong forms"
    },
    {
      "id": "statistical-foundations/likelihood-ratio-tests",
      "title": "Likelihood Ratio Tests",
      "url": "tutorials/statistical-foundations/likelihood-ratio-tests.html",
      "topic": "statistical-foundations",
      "tags": [
        "likelihood-ratio",
        "wilks",
        "nested-models",
        "hypothesis-test",
        "topic:statistical-foundations",
        "hypothesis-testing",
        "diagnostic-accuracy",
        "asymptotic-theory"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Comparing nested models by the ratio of maximised likelihoods, with Wilks' asymptotic chi-squared"
    },
    {
      "id": "statistical-foundations/maximum-likelihood-estimation",
      "title": "Maximum Likelihood Estimation",
      "url": "tutorials/statistical-foundations/maximum-likelihood-estimation.html",
      "topic": "statistical-foundations",
      "tags": [
        "mle",
        "likelihood",
        "score",
        "fisher-information",
        "topic:statistical-foundations",
        "asymptotic-theory"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The likelihood function, score equations, and asymptotic properties of the MLE"
    },
    {
      "id": "statistical-foundations/method-of-moments",
      "title": "The Method of Moments",
      "url": "tutorials/statistical-foundations/method-of-moments.html",
      "topic": "statistical-foundations",
      "tags": [
        "method-of-moments",
        "estimator",
        "moments",
        "topic:statistical-foundations"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Estimate parameters by equating theoretical moments to their sample counterparts"
    },
    {
      "id": "statistical-foundations/moments-and-mgfs",
      "title": "Moments and Moment Generating Functions",
      "url": "tutorials/statistical-foundations/moments-and-mgfs.html",
      "topic": "statistical-foundations",
      "tags": [
        "moments",
        "mgf",
        "expectation",
        "generating-function",
        "topic:statistical-foundations",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Moments summarise distributions; the MGF is a generating function that often characterises them uniquely"
    },
    {
      "id": "statistical-foundations/order-statistics",
      "title": "Order Statistics",
      "url": "tutorials/statistical-foundations/order-statistics.html",
      "topic": "statistical-foundations",
      "tags": [
        "order-statistics",
        "quantiles",
        "extremes",
        "range",
        "topic:statistical-foundations",
        "robust-statistics",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Distributions of the sorted sample and their role in quantile theory and robust estimation"
    },
    {
      "id": "statistical-foundations/pivotal-quantities",
      "title": "Pivotal Quantities",
      "url": "tutorials/statistical-foundations/pivotal-quantities.html",
      "topic": "statistical-foundations",
      "tags": [
        "pivot",
        "confidence-interval",
        "exact-inference",
        "topic:statistical-foundations",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Functions of data and parameter whose distribution does not depend on the parameter, and the CIs they produce"
    },
    {
      "id": "statistical-foundations/population-vs-sample",
      "title": "Population vs. Sample",
      "url": "tutorials/statistical-foundations/population-vs-sample.html",
      "topic": "statistical-foundations",
      "tags": [
        "population",
        "sample",
        "parameter",
        "statistic",
        "sampling-frame",
        "topic:statistical-foundations"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Target population, sampling frame, and the distinction between parameters and statistics"
    },
    {
      "id": "statistical-foundations/sampling-distributions",
      "title": "Sampling Distributions",
      "url": "tutorials/statistical-foundations/sampling-distributions.html",
      "topic": "statistical-foundations",
      "tags": [
        "sampling-distribution",
        "statistic",
        "standard-error",
        "variability",
        "topic:statistical-foundations",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The distribution of a statistic across repeated samples: the key object that makes inference possible"
    },
    {
      "id": "statistical-foundations/sampling-methods",
      "title": "Sampling Methods",
      "url": "tutorials/statistical-foundations/sampling-methods.html",
      "topic": "statistical-foundations",
      "tags": [
        "sampling",
        "srs",
        "stratified",
        "cluster",
        "systematic",
        "topic:statistical-foundations",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Simple random, stratified, cluster, systematic, and convenience sampling -- and when each is appropriate"
    },
    {
      "id": "statistical-foundations/scales-of-measurement",
      "title": "Scales of Measurement",
      "url": "tutorials/statistical-foundations/scales-of-measurement.html",
      "topic": "statistical-foundations",
      "tags": [
        "scales",
        "measurement",
        "nominal",
        "ordinal",
        "interval",
        "ratio",
        "topic:statistical-foundations"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Nominal, ordinal, interval, and ratio scales, and which statistics each legitimately supports"
    },
    {
      "id": "statistical-foundations/slutsky-theorem",
      "title": "Slutsky's Theorem",
      "url": "tutorials/statistical-foundations/slutsky-theorem.html",
      "topic": "statistical-foundations",
      "tags": [
        "slutsky",
        "asymptotic",
        "convergence",
        "topic:statistical-foundations",
        "probability-and-distributions",
        "asymptotic-theory"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Combining convergence in distribution and in probability for asymptotic arguments"
    },
    {
      "id": "statistical-foundations/standard-error",
      "title": "The Standard Error",
      "url": "tutorials/statistical-foundations/standard-error.html",
      "topic": "statistical-foundations",
      "tags": [
        "standard-error",
        "sampling-distribution",
        "bootstrap",
        "inference",
        "topic:statistical-foundations",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The standard deviation of a statistic's sampling distribution, and how to compute it in R"
    },
    {
      "id": "statistical-foundations/sufficient-statistics",
      "title": "Sufficient Statistics",
      "url": "tutorials/statistical-foundations/sufficient-statistics.html",
      "topic": "statistical-foundations",
      "tags": [
        "sufficiency",
        "factorisation-theorem",
        "exponential-family",
        "topic:statistical-foundations"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Summaries that capture every bit of information about a parameter, with the factorisation theorem and exponential family"
    },
    {
      "id": "statistical-foundations/unbiasedness-consistency-efficiency",
      "title": "Unbiasedness, Consistency, Efficiency",
      "url": "tutorials/statistical-foundations/unbiasedness-consistency-efficiency.html",
      "topic": "statistical-foundations",
      "tags": [
        "unbiased",
        "consistency",
        "efficiency",
        "estimator",
        "topic:statistical-foundations"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Three core finite- and large-sample properties that let us compare estimators"
    },
    {
      "id": "survival-analysis/accelerated-failure-time",
      "title": "Accelerated Failure Time Models",
      "url": "tutorials/survival-analysis/accelerated-failure-time.html",
      "topic": "survival-analysis",
      "tags": [
        "aft",
        "parametric-survival",
        "topic:survival-analysis",
        "regression",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Parametric survival regression on the log-time scale with acceleration-factor interpretation"
    },
    {
      "id": "survival-analysis/brier-score-survival",
      "title": "The Brier Score for Survival",
      "url": "tutorials/survival-analysis/brier-score-survival.html",
      "topic": "survival-analysis",
      "tags": [
        "brier-score",
        "prediction-loss",
        "calibration",
        "topic:survival-analysis",
        "time-to-event",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Time-dependent prediction loss combining calibration and discrimination"
    },
    {
      "id": "survival-analysis/censoring-types",
      "title": "Censoring Types",
      "url": "tutorials/survival-analysis/censoring-types.html",
      "topic": "survival-analysis",
      "tags": [
        "censoring",
        "right-censored",
        "interval-censored",
        "topic:survival-analysis",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Right, left, interval, and informative censoring in time-to-event data"
    },
    {
      "id": "survival-analysis/competing-risks-cif",
      "title": "Competing Risks: Cumulative Incidence",
      "url": "tutorials/survival-analysis/competing-risks-cif.html",
      "topic": "survival-analysis",
      "tags": [
        "competing-risks",
        "cif",
        "cmprsk",
        "topic:survival-analysis",
        "time-to-event",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Estimating the probability of each event type in the presence of competing risks"
    },
    {
      "id": "survival-analysis/concordance-c-index",
      "title": "Concordance and the C-Index",
      "url": "tutorials/survival-analysis/concordance-c-index.html",
      "topic": "survival-analysis",
      "tags": [
        "c-index",
        "concordance",
        "discrimination",
        "topic:survival-analysis",
        "time-to-event",
        "agreement-and-reliability",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Discrimination ability of a survival model measured by concordance probability"
    },
    {
      "id": "survival-analysis/conditional-survival",
      "title": "Conditional Survival",
      "url": "tutorials/survival-analysis/conditional-survival.html",
      "topic": "survival-analysis",
      "tags": [
        "conditional-survival",
        "prognosis",
        "topic:survival-analysis",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Updating prognosis for survivors: P(T > t + s | T > s)"
    },
    {
      "id": "survival-analysis/cox-assumptions-check",
      "title": "Checking Cox Assumptions",
      "url": "tutorials/survival-analysis/cox-assumptions-check.html",
      "topic": "survival-analysis",
      "tags": [
        "cox-diagnostics",
        "schoenfeld",
        "martingale",
        "topic:survival-analysis",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Diagnostics for proportional hazards, functional form, and influential observations"
    },
    {
      "id": "survival-analysis/cox-proportional-hazards",
      "title": "Cox Proportional Hazards Regression",
      "url": "tutorials/survival-analysis/cox-proportional-hazards.html",
      "topic": "survival-analysis",
      "tags": [
        "cox",
        "proportional-hazards",
        "partial-likelihood",
        "topic:survival-analysis",
        "regression",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Semi-parametric regression for survival data with arbitrary baseline hazard"
    },
    {
      "id": "survival-analysis/fine-gray-regression",
      "title": "Fine-Gray Subdistribution Hazards",
      "url": "tutorials/survival-analysis/fine-gray-regression.html",
      "topic": "survival-analysis",
      "tags": [
        "fine-gray",
        "subdistribution-hazards",
        "competing-risks",
        "topic:survival-analysis",
        "regression",
        "time-to-event",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Regression directly targeting cumulative incidence in competing-risks data"
    },
    {
      "id": "survival-analysis/flexible-parametric-royston-parmar",
      "title": "Royston-Parmar Flexible Parametric Models",
      "url": "tutorials/survival-analysis/flexible-parametric-royston-parmar.html",
      "topic": "survival-analysis",
      "tags": [
        "royston-parmar",
        "flexible-parametric",
        "splines",
        "topic:survival-analysis",
        "time-to-event",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Splines on the log-cumulative-hazard for flexible baseline without leaving parametric modelling"
    },
    {
      "id": "survival-analysis/frailty-models",
      "title": "Frailty Models",
      "url": "tutorials/survival-analysis/frailty-models.html",
      "topic": "survival-analysis",
      "tags": [
        "frailty",
        "random-effects",
        "clustered-survival",
        "topic:survival-analysis",
        "time-to-event",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Random effects in survival: unobserved heterogeneity and clustered survival data"
    },
    {
      "id": "survival-analysis/hazard-survival-cumulative",
      "title": "Hazard, Survival, and Cumulative Hazard",
      "url": "tutorials/survival-analysis/hazard-survival-cumulative.html",
      "topic": "survival-analysis",
      "tags": [
        "hazard",
        "survival-function",
        "cumulative-hazard",
        "topic:survival-analysis",
        "time-to-event",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The three equivalent representations of a time-to-event distribution"
    },
    {
      "id": "survival-analysis/interval-censored-data",
      "title": "Interval-Censored Data",
      "url": "tutorials/survival-analysis/interval-censored-data.html",
      "topic": "survival-analysis",
      "tags": [
        "interval-censoring",
        "turnbull",
        "npmle",
        "topic:survival-analysis",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Events known to occur within a time interval, not at an exact time"
    },
    {
      "id": "survival-analysis/joint-longitudinal-survival",
      "title": "Joint Longitudinal-Survival Models",
      "url": "tutorials/survival-analysis/joint-longitudinal-survival.html",
      "topic": "survival-analysis",
      "tags": [
        "joint-model",
        "longitudinal",
        "shared-random-effects",
        "topic:survival-analysis",
        "time-to-event",
        "longitudinal-and-mixed-models"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Simultaneously modelling a longitudinal biomarker and time-to-event outcome"
    },
    {
      "id": "survival-analysis/kaplan-meier",
      "title": "Kaplan-Meier Estimation",
      "url": "tutorials/survival-analysis/kaplan-meier.html",
      "topic": "survival-analysis",
      "tags": [
        "kaplan-meier",
        "survival-function",
        "censoring",
        "log-rank-test",
        "topic:survival-analysis",
        "non-parametric",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Non-parametric estimation of the survival function from right-censored data, with confidence intervals and group comparisons"
    },
    {
      "id": "survival-analysis/landmark-analysis",
      "title": "Landmark Analysis",
      "url": "tutorials/survival-analysis/landmark-analysis.html",
      "topic": "survival-analysis",
      "tags": [
        "landmark",
        "time-varying",
        "topic:survival-analysis",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Conditioning on survival to a landmark time to analyse time-varying covariates"
    },
    {
      "id": "survival-analysis/left-truncation",
      "title": "Left Truncation",
      "url": "tutorials/survival-analysis/left-truncation.html",
      "topic": "survival-analysis",
      "tags": [
        "left-truncation",
        "delayed-entry",
        "age-as-timescale",
        "topic:survival-analysis",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Delayed entry: subjects only enter the study after surviving to a certain age or time"
    },
    {
      "id": "survival-analysis/log-rank-test-details",
      "title": "Log-Rank Test: Details",
      "url": "tutorials/survival-analysis/log-rank-test-details.html",
      "topic": "survival-analysis",
      "tags": [
        "log-rank",
        "survival-comparison",
        "topic:survival-analysis",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Weighted and stratified log-rank test for comparing survival across groups"
    },
    {
      "id": "survival-analysis/multi-state-models",
      "title": "Multi-State Models",
      "url": "tutorials/survival-analysis/multi-state-models.html",
      "topic": "survival-analysis",
      "tags": [
        "multi-state",
        "transition",
        "mstate",
        "topic:survival-analysis",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Modelling transitions between discrete states over time"
    },
    {
      "id": "survival-analysis/nelson-aalen-estimator",
      "title": "The Nelson-Aalen Estimator",
      "url": "tutorials/survival-analysis/nelson-aalen-estimator.html",
      "topic": "survival-analysis",
      "tags": [
        "nelson-aalen",
        "cumulative-hazard",
        "estimator",
        "topic:survival-analysis",
        "non-parametric",
        "time-to-event",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Non-parametric estimator of the cumulative hazard under right-censoring"
    },
    {
      "id": "survival-analysis/parametric-survival-lognormal",
      "title": "Parametric Survival: Log-Normal",
      "url": "tutorials/survival-analysis/parametric-survival-lognormal.html",
      "topic": "survival-analysis",
      "tags": [
        "log-normal-survival",
        "parametric",
        "topic:survival-analysis",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Log-normal AFT model for non-monotone hazards"
    },
    {
      "id": "survival-analysis/parametric-survival-weibull",
      "title": "Parametric Survival: Weibull",
      "url": "tutorials/survival-analysis/parametric-survival-weibull.html",
      "topic": "survival-analysis",
      "tags": [
        "weibull",
        "parametric-survival",
        "aft",
        "topic:survival-analysis",
        "regression",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Fitting Weibull regression in both PH and AFT parameterisations"
    },
    {
      "id": "survival-analysis/recurrent-events",
      "title": "Recurrent Events",
      "url": "tutorials/survival-analysis/recurrent-events.html",
      "topic": "survival-analysis",
      "tags": [
        "recurrent-events",
        "andersen-gill",
        "pwp",
        "topic:survival-analysis",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Modelling repeated events per subject: Andersen-Gill, PWP, and frailty approaches"
    },
    {
      "id": "survival-analysis/restricted-mean-survival-time",
      "title": "Restricted Mean Survival Time (RMST)",
      "url": "tutorials/survival-analysis/restricted-mean-survival-time.html",
      "topic": "survival-analysis",
      "tags": [
        "rmst",
        "mean-survival",
        "topic:survival-analysis",
        "time-to-event",
        "robust-statistics",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Alternative summary robust to non-proportional hazards and censoring"
    },
    {
      "id": "survival-analysis/schoenfeld-residuals",
      "title": "Schoenfeld Residuals",
      "url": "tutorials/survival-analysis/schoenfeld-residuals.html",
      "topic": "survival-analysis",
      "tags": [
        "schoenfeld",
        "proportional-hazards",
        "topic:survival-analysis",
        "regression",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Residuals that test the proportional-hazards assumption in Cox regression"
    },
    {
      "id": "survival-analysis/stratified-cox",
      "title": "Stratified Cox Models",
      "url": "tutorials/survival-analysis/stratified-cox.html",
      "topic": "survival-analysis",
      "tags": [
        "stratified-cox",
        "non-proportional",
        "baseline",
        "topic:survival-analysis",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Stratum-specific baseline hazards when proportional hazards fails across groups"
    },
    {
      "id": "survival-analysis/survival-simulation",
      "title": "Simulating Survival Data",
      "url": "tutorials/survival-analysis/survival-simulation.html",
      "topic": "survival-analysis",
      "tags": [
        "simulation",
        "inverse-hazard",
        "topic:survival-analysis",
        "power-and-sample-size",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Generating realistic censored time-to-event datasets for power analysis and method validation"
    },
    {
      "id": "survival-analysis/time-dependent-roc",
      "title": "Time-Dependent ROC",
      "url": "tutorials/survival-analysis/time-dependent-roc.html",
      "topic": "survival-analysis",
      "tags": [
        "time-dependent-roc",
        "auc",
        "timeROC",
        "topic:survival-analysis",
        "time-to-event",
        "diagnostic-accuracy"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "ROC curves and AUC evaluated at specific follow-up times for survival predictions"
    },
    {
      "id": "survival-analysis/time-varying-covariates",
      "title": "Time-Varying Covariates",
      "url": "tutorials/survival-analysis/time-varying-covariates.html",
      "topic": "survival-analysis",
      "tags": [
        "time-varying-covariates",
        "counting-process",
        "topic:survival-analysis",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Covariates whose value changes during follow-up: counting-process data layout"
    },
    {
      "id": "survival-analysis/weighted-log-rank",
      "title": "Weighted Log-Rank Tests",
      "url": "tutorials/survival-analysis/weighted-log-rank.html",
      "topic": "survival-analysis",
      "tags": [
        "weighted-log-rank",
        "peto-peto",
        "fleming-harrington",
        "topic:survival-analysis",
        "time-to-event"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Peto-Peto, Fleming-Harrington, and other weighted variants for non-proportional hazards"
    },
    {
      "id": "time-series/acf-pacf-interpretation",
      "title": "Interpreting ACF and PACF",
      "url": "tutorials/time-series/acf-pacf-interpretation.html",
      "topic": "time-series",
      "tags": [
        "acf",
        "pacf",
        "model-identification",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Identifying AR and MA orders from autocorrelation and partial autocorrelation functions"
    },
    {
      "id": "time-series/adf-test",
      "title": "Augmented Dickey-Fuller Test",
      "url": "tutorials/time-series/adf-test.html",
      "topic": "time-series",
      "tags": [
        "adf",
        "unit-root",
        "stationarity",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Testing for unit roots / non-stationarity against a stationary alternative"
    },
    {
      "id": "time-series/arima-models",
      "title": "ARIMA Models",
      "url": "tutorials/time-series/arima-models.html",
      "topic": "time-series",
      "tags": [
        "arima",
        "sarima",
        "forecasting",
        "stationarity",
        "time-series",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Autoregressive integrated moving average models for forecasting stationary and trending time series"
    },
    {
      "id": "time-series/bcp-bayesian-changepoint",
      "title": "Bayesian Changepoint Detection",
      "url": "tutorials/time-series/bcp-bayesian-changepoint.html",
      "topic": "time-series",
      "tags": [
        "bayesian-changepoint",
        "bcp",
        "online",
        "topic:time-series-analysis",
        "bayesian-methods",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Posterior probability of a changepoint at each time, with online and offline variants"
    },
    {
      "id": "time-series/changepoint-detection",
      "title": "Changepoint Detection",
      "url": "tutorials/time-series/changepoint-detection.html",
      "topic": "time-series",
      "tags": [
        "changepoint",
        "pelt",
        "segmentation",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Identifying abrupt shifts in mean, variance, or slope of a time series"
    },
    {
      "id": "time-series/diebold-mariano",
      "title": "Diebold-Mariano Test",
      "url": "tutorials/time-series/diebold-mariano.html",
      "topic": "time-series",
      "tags": [
        "diebold-mariano",
        "forecast-comparison",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Testing equality of forecast accuracy between two competing models"
    },
    {
      "id": "time-series/differencing",
      "title": "Differencing a Time Series",
      "url": "tutorials/time-series/differencing.html",
      "topic": "time-series",
      "tags": [
        "differencing",
        "trend",
        "seasonal-difference",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Removing trend via first differences; seasonal differencing for cyclic patterns"
    },
    {
      "id": "time-series/ets-models",
      "title": "ETS Models",
      "url": "tutorials/time-series/ets-models.html",
      "topic": "time-series",
      "tags": [
        "ets",
        "state-space",
        "exponential-smoothing",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "State-space formulation of exponential smoothing: Error, Trend, Seasonal"
    },
    {
      "id": "time-series/exponential-smoothing",
      "title": "Exponential Smoothing",
      "url": "tutorials/time-series/exponential-smoothing.html",
      "topic": "time-series",
      "tags": [
        "exponential-smoothing",
        "holt",
        "holt-winters",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Recursive weighted-average smoothers: simple, Holt (trend), Holt-Winters (seasonal)"
    },
    {
      "id": "time-series/forecast-accuracy-metrics",
      "title": "Forecast Accuracy Metrics",
      "url": "tutorials/time-series/forecast-accuracy-metrics.html",
      "topic": "time-series",
      "tags": [
        "mae",
        "rmse",
        "mape",
        "mase",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "MAE, RMSE, MAPE, MASE: scaled and scale-dependent measures of forecast error"
    },
    {
      "id": "time-series/garch-volatility",
      "title": "GARCH Models",
      "url": "tutorials/time-series/garch-volatility.html",
      "topic": "time-series",
      "tags": [
        "garch",
        "volatility",
        "conditional-variance",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Conditional heteroscedasticity models: volatility clustering in financial and other series"
    },
    {
      "id": "time-series/granger-causality",
      "title": "Granger Causality",
      "url": "tutorials/time-series/granger-causality.html",
      "topic": "time-series",
      "tags": [
        "granger",
        "causality",
        "predictive",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Testing whether one time series improves prediction of another beyond its own history"
    },
    {
      "id": "time-series/holt-winters",
      "title": "Holt-Winters Method",
      "url": "tutorials/time-series/holt-winters.html",
      "topic": "time-series",
      "tags": [
        "holt-winters",
        "seasonal",
        "exponential-smoothing",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Triple exponential smoothing with level, trend, and seasonal components"
    },
    {
      "id": "time-series/kalman-filter",
      "title": "The Kalman Filter",
      "url": "tutorials/time-series/kalman-filter.html",
      "topic": "time-series",
      "tags": [
        "kalman",
        "filtering",
        "state-space",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Recursive optimal estimation for linear-Gaussian state-space models"
    },
    {
      "id": "time-series/kpss-test",
      "title": "KPSS Test",
      "url": "tutorials/time-series/kpss-test.html",
      "topic": "time-series",
      "tags": [
        "kpss",
        "stationarity",
        "unit-root",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Testing for stationarity (null) against a unit-root alternative"
    },
    {
      "id": "time-series/ljung-box",
      "title": "The Ljung-Box Test",
      "url": "tutorials/time-series/ljung-box.html",
      "topic": "time-series",
      "tags": [
        "ljung-box",
        "portmanteau",
        "residual-autocorrelation",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Portmanteau test for remaining autocorrelation in residuals across multiple lags"
    },
    {
      "id": "time-series/moving-averages",
      "title": "Moving Averages",
      "url": "tutorials/time-series/moving-averages.html",
      "topic": "time-series",
      "tags": [
        "moving-average",
        "smoothing",
        "weighted",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Smoothing a time series by averaging over a sliding window"
    },
    {
      "id": "time-series/multivariate-var",
      "title": "Vector Autoregression (VAR)",
      "url": "tutorials/time-series/multivariate-var.html",
      "topic": "time-series",
      "tags": [
        "var",
        "multivariate",
        "granger",
        "topic:time-series-analysis",
        "multivariate-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Multivariate time series where each variable depends on lags of all variables"
    },
    {
      "id": "time-series/phillips-perron",
      "title": "Phillips-Perron Test",
      "url": "tutorials/time-series/phillips-perron.html",
      "topic": "time-series",
      "tags": [
        "phillips-perron",
        "unit-root",
        "non-parametric",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Non-parametric adjustment of the Dickey-Fuller test for autocorrelation and heteroscedasticity"
    },
    {
      "id": "time-series/rolling-origin-cv",
      "title": "Rolling-Origin Cross-Validation",
      "url": "tutorials/time-series/rolling-origin-cv.html",
      "topic": "time-series",
      "tags": [
        "cv",
        "rolling-origin",
        "time-series-cv",
        "topic:time-series-analysis",
        "machine-learning-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Time-series-aware cross-validation that respects temporal ordering"
    },
    {
      "id": "time-series/seasonal-arima",
      "title": "Seasonal ARIMA (SARIMA)",
      "url": "tutorials/time-series/seasonal-arima.html",
      "topic": "time-series",
      "tags": [
        "sarima",
        "seasonal-arima",
        "forecast",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "ARIMA extended with seasonal autoregressive, moving-average, and differencing terms"
    },
    {
      "id": "time-series/seasonal-decomposition-stl",
      "title": "Seasonal Decomposition (STL)",
      "url": "tutorials/time-series/seasonal-decomposition-stl.html",
      "topic": "time-series",
      "tags": [
        "stl",
        "decomposition",
        "loess",
        "topic:time-series-analysis",
        "robust-statistics"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Loess-based seasonal-trend decomposition robust to outliers"
    },
    {
      "id": "time-series/spectral-analysis",
      "title": "Spectral Analysis",
      "url": "tutorials/time-series/spectral-analysis.html",
      "topic": "time-series",
      "tags": [
        "spectral",
        "periodogram",
        "frequency-domain",
        "topic:time-series-analysis",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Decomposing a time series into frequency components via periodogram and spectral density"
    },
    {
      "id": "time-series/state-space-models",
      "title": "State-Space Models",
      "url": "tutorials/time-series/state-space-models.html",
      "topic": "time-series",
      "tags": [
        "state-space",
        "latent-state",
        "kalman",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Latent-state time-series framework unifying ARIMA, exponential smoothing, and many others"
    },
    {
      "id": "time-series/stationarity-tests",
      "title": "Stationarity Tests",
      "url": "tutorials/time-series/stationarity-tests.html",
      "topic": "time-series",
      "tags": [
        "stationarity",
        "unit-root",
        "adf",
        "kpss",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Testing whether a time series has constant mean and variance over time"
    },
    {
      "id": "time-series/time-series-introduction",
      "title": "Time Series: Introduction",
      "url": "tutorials/time-series/time-series-introduction.html",
      "topic": "time-series",
      "tags": [
        "time-series",
        "trend",
        "seasonality",
        "decomposition",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Trend, seasonality, cyclicality, and noise: the four components of a time series"
    },
    {
      "id": "time-series/vecm-cointegration",
      "title": "VECM and Cointegration",
      "url": "tutorials/time-series/vecm-cointegration.html",
      "topic": "time-series",
      "tags": [
        "vecm",
        "cointegration",
        "johansen",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Vector error correction models for cointegrated series; Johansen trace and eigenvalue tests"
    },
    {
      "id": "time-series/wavelet-analysis",
      "title": "Wavelet Analysis",
      "url": "tutorials/time-series/wavelet-analysis.html",
      "topic": "time-series",
      "tags": [
        "wavelet",
        "time-frequency",
        "cwt",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Time-frequency decomposition capturing localised periodicities that change over time"
    },
    {
      "id": "time-series/white-noise-tests",
      "title": "White Noise Tests",
      "url": "tutorials/time-series/white-noise-tests.html",
      "topic": "time-series",
      "tags": [
        "white-noise",
        "residual-diagnostics",
        "portmanteau",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Testing a residual series for independence and constant variance"
    },
    {
      "id": "time-series/x13-decomposition",
      "title": "X-13ARIMA-SEATS Decomposition",
      "url": "tutorials/time-series/x13-decomposition.html",
      "topic": "time-series",
      "tags": [
        "x13",
        "census",
        "seasonal-adjustment",
        "topic:time-series-analysis"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The seasonal adjustment tool used by US Census Bureau and statistical agencies"
    },
    {
      "id": "visualisation/aesthetics-and-geoms",
      "title": "Aesthetics and Geoms",
      "url": "tutorials/visualisation/aesthetics-and-geoms.html",
      "topic": "visualisation",
      "tags": [
        "ggplot2",
        "aesthetics",
        "geoms",
        "layered-grammar",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The two ggplot2 building blocks: what data to map (aesthetics) and how to draw it (geoms)"
    },
    {
      "id": "visualisation/annotations-and-labels",
      "title": "Annotations and Labels",
      "url": "tutorials/visualisation/annotations-and-labels.html",
      "topic": "visualisation",
      "tags": [
        "annotation",
        "ggtext",
        "ggrepel",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Adding text, arrows, rectangles, and repelling labels to ggplot2 figures"
    },
    {
      "id": "visualisation/bar-charts",
      "title": "Bar Charts",
      "url": "tutorials/visualisation/bar-charts.html",
      "topic": "visualisation",
      "tags": [
        "bar-chart",
        "geom_bar",
        "geom_col",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Counts per category and summarised values per category"
    },
    {
      "id": "visualisation/bland-altman-plots",
      "title": "Bland-Altman Plots",
      "url": "tutorials/visualisation/bland-altman-plots.html",
      "topic": "visualisation",
      "tags": [
        "bland-altman",
        "agreement",
        "limits-of-agreement",
        "topic:data-visualisation",
        "agreement-and-reliability",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Mean-vs-difference plot for comparing two measurement methods"
    },
    {
      "id": "visualisation/boxplots",
      "title": "Boxplots",
      "url": "tutorials/visualisation/boxplots.html",
      "topic": "visualisation",
      "tags": [
        "boxplot",
        "five-number-summary",
        "tukey",
        "topic:data-visualisation",
        "robust-statistics",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Five-number summary display: Tukey boxplots with whiskers and outlier rules"
    },
    {
      "id": "visualisation/bubble-plots",
      "title": "Bubble Plots",
      "url": "tutorials/visualisation/bubble-plots.html",
      "topic": "visualisation",
      "tags": [
        "bubble",
        "size-aesthetic",
        "scatter",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Scatter plots with a third variable encoded by point size"
    },
    {
      "id": "visualisation/colour-blind-safe-plots",
      "title": "Colour-Blind-Safe Plots",
      "url": "tutorials/visualisation/colour-blind-safe-plots.html",
      "topic": "visualisation",
      "tags": [
        "accessibility",
        "colour-blind",
        "palette",
        "wcag",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Designing plots that remain readable to viewers with colour-vision deficiencies"
    },
    {
      "id": "visualisation/colour-palettes",
      "title": "Colour Palettes",
      "url": "tutorials/visualisation/colour-palettes.html",
      "topic": "visualisation",
      "tags": [
        "colour",
        "palette",
        "viridis",
        "brewer",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Discrete, continuous, perceptually uniform, and diverging palettes for ggplot2"
    },
    {
      "id": "visualisation/contour-plots",
      "title": "Contour Plots",
      "url": "tutorials/visualisation/contour-plots.html",
      "topic": "visualisation",
      "tags": [
        "contour",
        "2d-density",
        "isolines",
        "topic:data-visualisation",
        "exploratory-and-descriptive",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Isolines of a 2D scalar field or density"
    },
    {
      "id": "visualisation/correlation-heatmaps",
      "title": "Correlation Heatmaps",
      "url": "tutorials/visualisation/correlation-heatmaps.html",
      "topic": "visualisation",
      "tags": [
        "correlation-matrix",
        "corrplot",
        "ggcorrplot",
        "topic:data-visualisation",
        "hypothesis-testing",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Visualising a correlation matrix with colour-encoded cells and significance markers"
    },
    {
      "id": "visualisation/density-plots",
      "title": "Density Plots",
      "url": "tutorials/visualisation/density-plots.html",
      "topic": "visualisation",
      "tags": [
        "density",
        "kde",
        "distribution",
        "topic:data-visualisation",
        "exploratory-and-descriptive",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Smooth distribution display via kernel density estimation"
    },
    {
      "id": "visualisation/dot-plots",
      "title": "Dot Plots",
      "url": "tutorials/visualisation/dot-plots.html",
      "topic": "visualisation",
      "tags": [
        "dot-plot",
        "cleveland",
        "wilkinson",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Cleveland dot plots and Wilkinson dot plots for compact distributional displays"
    },
    {
      "id": "visualisation/facets-and-panels",
      "title": "Facets and Panels",
      "url": "tutorials/visualisation/facets-and-panels.html",
      "topic": "visualisation",
      "tags": [
        "ggplot2",
        "facet",
        "small-multiples",
        "panels",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Creating small multiples via facet_wrap and facet_grid"
    },
    {
      "id": "visualisation/forest-plots-viz",
      "title": "Forest Plots (Visualisation)",
      "url": "tutorials/visualisation/forest-plots-viz.html",
      "topic": "visualisation",
      "tags": [
        "forest-plot",
        "meta-analysis",
        "subgroup",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Point estimates and confidence intervals stacked across studies or subgroups"
    },
    {
      "id": "visualisation/funnel-plots-viz",
      "title": "Funnel Plots (Visualisation)",
      "url": "tutorials/visualisation/funnel-plots-viz.html",
      "topic": "visualisation",
      "tags": [
        "funnel-plot",
        "publication-bias",
        "meta-analysis",
        "topic:data-visualisation",
        "exploratory-and-descriptive",
        "meta-analysis-methods"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Study effect vs. precision plot used to detect publication bias in meta-analysis"
    },
    {
      "id": "visualisation/ggplot-themes",
      "title": "ggplot2 Themes",
      "url": "tutorials/visualisation/ggplot-themes.html",
      "topic": "visualisation",
      "tags": [
        "ggplot2",
        "theme",
        "styling",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Visual styling of plots via complete themes and fine-grained theme() elements"
    },
    {
      "id": "visualisation/ggsave-and-export",
      "title": "Saving and Exporting Figures",
      "url": "tutorials/visualisation/ggsave-and-export.html",
      "topic": "visualisation",
      "tags": [
        "ggsave",
        "export",
        "dpi",
        "tiff",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Exporting ggplot figures to PDF, PNG, SVG, and TIFF with the right dimensions and DPI"
    },
    {
      "id": "visualisation/grammar-of-graphics",
      "title": "The Grammar of Graphics",
      "url": "tutorials/visualisation/grammar-of-graphics.html",
      "topic": "visualisation",
      "tags": [
        "ggplot2",
        "grammar-of-graphics",
        "visualisation",
        "layered-graphics",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Understanding ggplot2 as a layered grammar: data, mappings, geoms, stats, scales, coordinates, facets, and themes"
    },
    {
      "id": "visualisation/heatmaps",
      "title": "Heatmaps",
      "url": "tutorials/visualisation/heatmaps.html",
      "topic": "visualisation",
      "tags": [
        "heatmap",
        "geom_tile",
        "pheatmap",
        "complexheatmap",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Matrix displays with colour-encoded cell values, optionally with row/column ordering"
    },
    {
      "id": "visualisation/hexbin-plots",
      "title": "Hexbin Plots",
      "url": "tutorials/visualisation/hexbin-plots.html",
      "topic": "visualisation",
      "tags": [
        "hexbin",
        "geom_hex",
        "large-n",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Binning the 2D plane into hexagons and colouring by count, for large bivariate data"
    },
    {
      "id": "visualisation/histograms",
      "title": "Histograms",
      "url": "tutorials/visualisation/histograms.html",
      "topic": "visualisation",
      "tags": [
        "histogram",
        "distribution",
        "bins",
        "topic:data-visualisation",
        "exploratory-and-descriptive",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Univariate distribution display via binned frequencies or densities"
    },
    {
      "id": "visualisation/interactive-ggiraph",
      "title": "Interactive Plots with ggiraph",
      "url": "tutorials/visualisation/interactive-ggiraph.html",
      "topic": "visualisation",
      "tags": [
        "interactive",
        "ggiraph",
        "svg",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Interactive SVG-based ggplot extensions with per-element hover, click, and selection"
    },
    {
      "id": "visualisation/interactive-plotly",
      "title": "Interactive Plots with plotly",
      "url": "tutorials/visualisation/interactive-plotly.html",
      "topic": "visualisation",
      "tags": [
        "interactive",
        "plotly",
        "ggplotly",
        "html",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Converting ggplot objects to interactive HTML plots via ggplotly()"
    },
    {
      "id": "visualisation/line-plots",
      "title": "Line Plots",
      "url": "tutorials/visualisation/line-plots.html",
      "topic": "visualisation",
      "tags": [
        "line",
        "time-series",
        "trajectory",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Connecting ordered observations with lines for time-series and trajectory displays"
    },
    {
      "id": "visualisation/pairs-plots",
      "title": "Pairs Plots",
      "url": "tutorials/visualisation/pairs-plots.html",
      "topic": "visualisation",
      "tags": [
        "pairs-plot",
        "ggpairs",
        "scatterplot-matrix",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Scatterplot matrices for exploring pairwise relationships among several continuous variables"
    },
    {
      "id": "visualisation/patchwork-composition",
      "title": "Patchwork: Multi-Plot Composition",
      "url": "tutorials/visualisation/patchwork-composition.html",
      "topic": "visualisation",
      "tags": [
        "patchwork",
        "composition",
        "multi-panel",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Composing multiple ggplot objects into a single figure with the patchwork package"
    },
    {
      "id": "visualisation/raincloud-plots",
      "title": "Raincloud Plots",
      "url": "tutorials/visualisation/raincloud-plots.html",
      "topic": "visualisation",
      "tags": [
        "raincloud",
        "ggdist",
        "distribution",
        "topic:data-visualisation",
        "exploratory-and-descriptive",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Half-violin + jittered raw points + boxplot: a comprehensive distribution display"
    },
    {
      "id": "visualisation/ridge-plots",
      "title": "Ridge Plots",
      "url": "tutorials/visualisation/ridge-plots.html",
      "topic": "visualisation",
      "tags": [
        "ridge",
        "ggridges",
        "density",
        "stacked",
        "topic:data-visualisation",
        "regression",
        "exploratory-and-descriptive",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Stacked density curves across groups, also called 'joy plots"
    },
    {
      "id": "visualisation/roc-curves-plot",
      "title": "ROC Curves",
      "url": "tutorials/visualisation/roc-curves-plot.html",
      "topic": "visualisation",
      "tags": [
        "roc",
        "auc",
        "diagnostic",
        "classifier",
        "topic:data-visualisation",
        "diagnostic-accuracy",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Sensitivity vs 1-specificity plots for diagnostic tests and binary classifiers"
    },
    {
      "id": "visualisation/scales-and-coordinates",
      "title": "Scales and Coordinates",
      "url": "tutorials/visualisation/scales-and-coordinates.html",
      "topic": "visualisation",
      "tags": [
        "ggplot2",
        "scales",
        "coordinates",
        "axes",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Controlling how data values are mapped to visual values (scales) and how the plot area is organised (coordinates)"
    },
    {
      "id": "visualisation/scatter-plots",
      "title": "Scatter Plots",
      "url": "tutorials/visualisation/scatter-plots.html",
      "topic": "visualisation",
      "tags": [
        "scatter",
        "geom_point",
        "bivariate",
        "overplotting",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "The bivariate continuous default: geom_point with overplotting strategies and trend lines"
    },
    {
      "id": "visualisation/stacked-dodged-bars",
      "title": "Stacked and Dodged Bars",
      "url": "tutorials/visualisation/stacked-dodged-bars.html",
      "topic": "visualisation",
      "tags": [
        "stacked-bars",
        "dodged-bars",
        "fill",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Two-factor bar charts: stacking for composition, dodging for side-by-side comparison"
    },
    {
      "id": "visualisation/survival-curves-plot",
      "title": "Survival Curves",
      "url": "tutorials/visualisation/survival-curves-plot.html",
      "topic": "visualisation",
      "tags": [
        "survival",
        "kaplan-meier",
        "survminer",
        "topic:data-visualisation",
        "time-to-event",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Publication-quality Kaplan-Meier plots with risk tables and log-rank annotation"
    },
    {
      "id": "visualisation/time-series-plots",
      "title": "Time Series Plots",
      "url": "tutorials/visualisation/time-series-plots.html",
      "topic": "visualisation",
      "tags": [
        "time-series",
        "dates",
        "forecasting",
        "topic:data-visualisation",
        "exploratory-and-descriptive"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Dated line plots with forecast bands and decomposition displays"
    },
    {
      "id": "visualisation/violin-plots",
      "title": "Violin Plots",
      "url": "tutorials/visualisation/violin-plots.html",
      "topic": "visualisation",
      "tags": [
        "violin",
        "density",
        "group-comparison",
        "topic:data-visualisation",
        "exploratory-and-descriptive",
        "probability-and-distributions"
      ],
      "labels": [],
      "date": "2026-04-17",
      "year": 2026,
      "summary": "Symmetric kernel density plots for comparing distributions across groups"
    }
  ],
  "edges": [
    {
      "source": "bayesian/bayes-factors",
      "target": "bayesian/bayes-hypothesis-testing",
      "weight": 3
    },
    {
      "source": "bayesian/bayes-factors",
      "target": "bayesian/savage-dickey-ratio",
      "weight": 3
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    {
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      "id": "multivariate",
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      "id": "one-sample",
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      "id": "overdispersion",
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      "id": "power-analysis",
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      "id": "proportional-hazards",
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      "id": "ranks",
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      "id": "resampling",
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      "id": "residuals",
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      "id": "rna-seq",
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      "id": "state-space",
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      "id": "survival",
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      "id": "tost",
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      "id": "tukey",
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      "id": "within-subject",
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      "id": "workflow",
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      "id": "2x2",
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      "id": "DE",
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      "id": "accessibility",
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      "id": "adf",
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      "id": "aft",
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      "id": "alpha-spending",
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      "id": "assumptions",
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      "id": "balance",
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      "id": "baseline",
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      "id": "bayes-theorem",
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      "id": "bayesian",
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      "id": "beta",
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      "id": "beta-binomial",
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      "id": "binomial",
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      "id": "bioequivalence",
      "label": "bioequivalence",
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      "id": "biplot",
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      "id": "boundary",
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      "id": "cdf",
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      "id": "censoring",
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      "id": "cluster-number",
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      "id": "cluster-rct",
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      "id": "cohen-h",
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      "id": "competing-risks",
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      "id": "concordance",
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      "id": "contingency",
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      "id": "contingency-table",
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      "id": "convolution",
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      "id": "count-data",
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      "id": "covariance",
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      "id": "cox",
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      "id": "credible-interval",
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      "id": "cross-validation",
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      "id": "cumulative-hazard",
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      "id": "cv",
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      "id": "dbscan",
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      "id": "deep-learning",
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      "id": "dependence",
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      "id": "design-effect",
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      "id": "differential-expression",
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      "id": "discrimination",
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      "id": "distance",
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      "id": "doe",
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      "id": "elastic-net",
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      "id": "enrichment",
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      "id": "eta-squared",
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      "id": "exact",
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      "id": "family-wise-error",
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      "id": "feature-engineering",
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      "id": "fisher-exact",
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      "id": "five-number-summary",
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      "id": "forecasting",
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      "id": "fractional",
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      "id": "fwer",
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      "id": "gaussian",
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      "id": "granger",
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      "id": "hard-to-change",
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      "id": "heatmap",
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      "id": "hmc",
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      "id": "holt-winters",
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      "id": "hypergeometric",
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      "id": "imbalanced",
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      "id": "inference",
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      "id": "interactions",
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      "id": "interactive",
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      "id": "interim",
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      "id": "iqr",
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      "id": "joint-distribution",
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      "id": "kalman",
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      "id": "kappa",
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      "id": "kmeans",
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      "id": "knn",
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      "id": "kpss",
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      "id": "kurtosis",
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      "id": "lasso",
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      "id": "lavaan",
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      "id": "lda",
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      "id": "leave-one-out",
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      "id": "likelihood",
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      "id": "likelihood-ratio",
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      "id": "limits-of-agreement",
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      "id": "linearity",
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      "id": "lmer",
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      "id": "log-rank",
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      "id": "loss",
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      "id": "mad",
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      "id": "mahalanobis",
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      "id": "marginal",
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      "id": "mcnemar",
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      "id": "memoryless",
      "label": "memoryless",
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      "id": "method-comparison",
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      "id": "microbiome",
      "label": "microbiome",
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    {
      "id": "mixed",
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    {
      "id": "mnar",
      "label": "mnar",
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      "id": "moments",
      "label": "moments",
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      "id": "multinomial",
      "label": "multinomial",
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      "id": "multiplicative",
      "label": "multiplicative",
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      "id": "normalization",
      "label": "normalization",
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      "id": "nuisance",
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      "id": "odds-ratio",
      "label": "odds-ratio",
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      "id": "offset",
      "label": "offset",
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      "id": "ols",
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      "id": "one-sided",
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      "id": "orthogonal-array",
      "label": "orthogonal-array",
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      "id": "paired-binary",
      "label": "paired-binary",
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      "id": "paired-t-test",
      "label": "paired-t-test",
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      "id": "pairs-plot",
      "label": "pairs-plot",
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      "id": "palette",
      "label": "palette",
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      "id": "parametric-survival",
      "label": "parametric-survival",
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      "id": "pca",
      "label": "pca",
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      "id": "pearson",
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      "id": "percentile",
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      "id": "permutation",
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      "id": "point-estimate",
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      "id": "poisson",
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      "id": "population",
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      "id": "portmanteau",
      "label": "portmanteau",
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      "id": "posterior-predictive",
      "label": "posterior-predictive",
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      "id": "proportions",
      "label": "proportions",
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      "id": "qc",
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      "id": "quadratic",
      "label": "quadratic",
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      "id": "r-squared",
      "label": "r-squared",
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      "id": "rbf",
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      "id": "recipes",
      "label": "recipes",
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      "id": "repeated-measures",
      "label": "repeated-measures",
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      "id": "rmanova",
      "label": "rmanova",
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      "id": "rotation",
      "label": "rotation",
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      "id": "rstanarm",
      "label": "rstanarm",
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      "id": "sample",
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      "id": "sarima",
      "label": "sarima",
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      "id": "scales",
      "label": "scales",
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      "id": "scatter",
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      "id": "scheffe",
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      "id": "schoenfeld",
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      "id": "score",
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      "id": "screening",
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      "id": "sd",
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      "id": "segmentation",
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      "id": "sequence",
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      "id": "sequential",
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      "id": "shrinkage",
      "label": "shrinkage",
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      "id": "simulation",
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    {
      "id": "skewness",
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      "id": "small-sample",
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      "id": "smooth",
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      "id": "specificity",
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      "id": "split-plot",
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      "id": "statistic",
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      "id": "stepped-wedge",
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      "id": "stratification",
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      "id": "stratified",
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      "id": "sva",
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      "id": "svm",
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      "id": "tau",
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      "id": "trajectory",
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      "id": "transformation",
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      "id": "trend",
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      "id": "variance-explained",
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      "id": "variants",
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      "id": "waiting-time",
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      "id": "weakly-informative",
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      "id": "weibull",
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      "id": "z-score",
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      "id": "z-test",
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      "count": 2
    },
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      "id": "16S",
      "label": "16S",
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    {
      "id": "2d-density",
      "label": "2d-density",
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      "id": "2k",
      "label": "2k",
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    {
      "id": "3k",
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    {
      "id": "ASV",
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    },
    {
      "id": "DADA2",
      "label": "DADA2",
      "count": 1
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    {
      "id": "DerSimonian-Laird",
      "label": "DerSimonian-Laird",
      "count": 1
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    {
      "id": "GO",
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