jamovi-modules-vm

Each module's Releases page carries four .jmo files per version — one for each combination of jamovi series and CPU:

The same file works on macOS, Windows and Linux: jamovi's compatibility check covers the R version and the CPU, not the operating system. Check Help → About in jamovi if you are unsure which R yours bundles, then Modules → jamovi library → Sideload and select the .jmo.

conttables2xK

Extends jmv's Contingency Tables (Independent Samples) comparative measures — odds ratio, log odds ratio, relative risk, difference in proportions — from 2x2-only to 2xK tables.

conttablespaired2xK

Extends jmv's Paired Samples Contingency Tables (McNemar) to non-binary paired responses (RxR tables): paired odds ratio, difference in proportions, marginal percentages, and exact tests.

corrInspect

Redesign of jmv's Correlation Matrix with a tidy one-row-per-pair table, an inspector mode for one reference variable against several others, annotated scatterplots, and a colour-coded heatmap.

regInspect

Clone of jmv's Linear Regression adding observed-data points (partial residuals by default) and prediction intervals to the Estimated Marginal Means plots, plus a descriptive Plots tab (scatter/box plots, colour-by-group, scatterplot matrix) independent of the model blocks.

jmvplus

Small additions to Descriptives (coefficient of variation) and Independent Samples T-Test (Fisher-Snedecor F-test alongside Levene's).

jamovi-combo all-in-one

Bundles conttables2xK, conttablespaired2xK, corrInspect, regInspect and jmvplus into a single sideloadable .jmo, so installing all five is one Sideload instead of five. SNPstats, prognosis, jmsequential, condLogReg, studysim and clinpred are released separately.

SNPstats

SNP analysis for genetic epidemiology: allele/genotype frequencies, Hardy-Weinberg equilibrium, SNP-response association, linkage disequilibrium, haplotype analysis and polygenic risk scores. Based on the SNPstats web tool. Since v1.1.0 it also imports genotypes from PLINK (.bed/.bim/.fam, .ped/.map, .tped/.tfam) and VCF files straight into a jamovi dataset — the former standalone snpImport module, merged in as a third analysis after its jamovi review. Requires jamovi 28.4 or newer (since v1.2.1); on jamovi 28.3, use v1.2.0.

prognosis

Survival analysis for prognostic studies, with compact output aimed at teaching and reporting. Kaplan-Meier: median survival, survival at given times, log-rank and weighted rank tests (Gehan-Breslow, Tarone-Ware, Peto-Peto, trend) and survival plots with a number-at-risk table. Cox regression: univariable and multivariable hazard ratios, reference levels and covariate HRs per unit, per SD or per 10/100/1000 units chosen for each variable, interactions with subgroup HRs, proportional-hazards tests, forest plots and adjusted survival curves. Optional explanations for students; English, Spanish and Catalan. Comes with a tutorial for medical students (survival theory and worked examples in jamovi, on a teaching dataset).

jmsequential

Sequential designs and monitoring for clinical trials, sequential experiments and safety surveillance, with control of the type I and type II errors. Group sequential designs: classical and error-spending boundaries, triangular tests, binding or non-binding futility; means, proportions and time-to-event endpoints (with accrual and dropout); interim monitoring with repeated confidence intervals, conditional power and adjusted final inference. Phase II multistage designs (Simon two-stage, Fleming, Bayesian) with exact operating characteristics. Wald's SPRT and sequential t-tests, MaxSPRT for post-marketing safety surveillance, Bayesian sequential monitoring, and anytime-valid e-values and confidence sequences. Comes with a tutorial website on sequential methods, with each example worked in jamovi.

condLogReg

Conditional logistic regression for matched case-control studies: jmv's Binomial Logistic Regression with a matching variable, fitted by the conditional likelihood (as survival::clogit). Matched-set and descriptive tables, odds ratios per level with the reference at 1, univariable and multivariable, trend tests, covariate scaling and forest plots. English, Spanish and Catalan. Comes with a tutorial website with three matched studies worked in jamovi.

studysim

Simulated medical studies for teaching. Pick a clinical scenario from a library, an outcome and a study design (cross-sectional, cohort, case-control, matched, nested case-control, case-cohort, ecological, prognosis with competing risks; randomised, cluster, 2×2 factorial, crossover and longitudinal trials with dropout or interim looks; diagnostic accuracy with markers; before-after studies with regression to the mean; method comparison for Bland-Altman); the dataset opens in a new window, with confounders, mediators, effect modifiers and null variables built in, and an answer key gives the true direct and total effects and what crude, fully adjusted and DAG-adjusted analyses should find, with absolute effects of trials and diagnostic accuracy at a cut-off. Biases can be built in on purpose (recall, control selection, colliders, non-adherence, informative loss to follow-up, immortal time, spectrum and verification bias). Compare designs draws several designs from the same population and plots their estimates against the truth, also over hundreds of replicate studies (bias, power, coverage). 65 scenarios (including dentistry and podiatry), most replicating real studies (from open data, the author's teaching datasets or published tables, some papers chosen for critical reading), several outcomes per study, realism layer (missing data, measurement error, detection limits, data-entry errors). Measured variables, grouping into quantiles or cut points, missing data, student keys and class exports, a teacher code that locks the key, causal diagram. Scenarios are editable (YAML, Excel, JSON) and can be derived from a real dataset. English, Spanish and Catalan. Comes with a tutorial website.

clinpred

Clinical prediction models: develop and validate logistic, linear and Cox (risk by a horizon) prediction models, following Steyerberg, TRIPOD+AI and Riley et al., and validate published models read from a file. Splines, factors and interactions; baseline covariates, backward/forward selection or elastic net repeated in every resample, and incremental value over the baseline model; missing data by complete cases, missing indicator or multiple imputation. Discrimination, calibration, overall measures and decision curves with confidence intervals; bootstrap optimism correction or cross-validation, split-sample or external validation, model updating, prediction stability and sample-size adequacy. Effects with forest plots, model equation, Shapley R² decomposition, permutation importance and SHAP; predictions saved to the spreadsheet and the model exported as JSON. Requires jamovi 28.4 or newer. English, Spanish and Catalan. Comes with a tutorial website on prediction models, with every analysis worked in jamovi.

jamovi-skill not a module

A Claude Code skill for building jamovi modules, distilled from developing SNPstats and snpImport plus a review of dev.jamovi.org and the jamovi source.

ondemand-jamovi

An Open OnDemand interactive app that launches jamovi as a browser session on an HPC cluster.

jamovi

The jamovi project itself.