jamovi is more than a stats program, it’s a community of stats developers writing specialized modules. Inside jamovi you have access to these modules from the jamovi library; a public space or ‘app store’ where you can download modules important to your work.
Below are a list of modules available from the jamovi library. You can download them from here and sideload them into jamovi, but its easier if you access the jamovi library from inside jamovi itself. You can access the jamovi library inside jamovi through the Modules menu.
Note that the modules available for download below, for macOS and windows, will only work in the 2.3 series of jamovi.
General Analyses for Linear Models in jamovi2.6.6
A suite for estimation of linear models, such as the general linear model, linear mixed model, generalized linear models and generalized mixed models. For ech family, models can be estimated with categorical and/or continuous variables, with options to facilitate estimation of interactions, simple slopes, simple effects, post-hoc tests, contrast analysis and visualization of the results.
jsq - Bayesian Methods1.2.0
A suite of Bayesian statistical methods, including t-tests, ANOVAs, linear models, and contingency tables. These tests are a port of the Bayesian analyses from the JASP statistical software (see jasp-stats.org for more information).
Effect Sizes and Confidence Intervals for R and Jamovi0.9.4
This module is for estimation, providing analyses that focus on effect sizes, uncertainty, and synthesis. This module is designed for ease of use and provides high-quality visualizations that emphasize effect sizes and uncertainty. Currently, all analyses are frequentist, returning confidence intervals. This is an alpha release. Please submit feedback, bug reports, and feature requests via the links provided here: https://thenewstatistics.com/itns/esci/jesci/. 0.9.4 ---- * Updating analyses to build graphs from data directly; should eliminate problems occuring on OSx and some Chromebooks 0.9.3 ---- * Additional update to try to fix estimate correlation of OSx. Hopefully fixed this time. 0.9.2 ---- * Updated estimate correlation to hopefully prevent crashes on OSx - Thanks to JLove's suggested code * Split estimate means into descriptives and estimate means * Fixed a bug for Mdiff where cohen's d didn't take the conf.level argument
Walrus - Robust Statistical Methods1.0.2
A toolbox of common robust statistical tests, including robust descriptives, robust t-tests, and robust ANOVA. Walrus is based on the WRS2 package by Patrick Mair, which is in turn based on the scripts and work of Rand Wilcox. These analyses are described in depth in the book Introduction to Robust Estimation & Hypothesis Testing.
Two one-sided tests (TOST) procedures to test equivalence for t-tests and correlations. More information.
Survival Module of ClinicoPath for jamovi0.0.2.05
Analysis for Clinicopathological Research Survival Module of ClinicoPath for jamovi ClinicoPath help researchers to generate natural language summaries of their dataset, generate cross tables with statistical tests, and survival analysis with survival tables, survival plots, and natural language summaries.
Flexplot - Graphically Based Data Analysis0.7.2
The flexplot suite is a graphically-based set of tools for doing data analysis. Flexplot allows users to specify a formula and the software automatically choses what sort of graphic to present. glinmod allows linear modeling and, like flexplot, chooses an appropriate graphic to present alongside the statistical
Correlations suite for jamovi3.6.2
This module is a tool for calculating correlations such as Pearson, Partial, Tetrachoric, Polychoric, Spearman, Intraclass correlation, Bootstrap agreement, Multilevel correlation, Concordance correlation, Analytic Hierarchy Process, Correlation structure, and allows users to produce Gaussian Graphical Model and Partial plot.
Rasch Mixture, LCA, and Test Equating Analysis5.2.2
This module allows users to conduct Latent class analysis, Latent Profile Analysis, Rasch model,Linear Logisitic Test Model, Linear and Equipercentile Equating, and Rasch mixture model including model information,fit statistics,and bootstrap item fit.
Conventional and regression-based continuous norming methods for psychometric, biometric and medical test construction. Percentiles and norm scores are estimated via inverse normal transformation and modeled via Taylor polynomials. The continuous method reduces the necessary sample size, closes gaps between and within the norm groups and decreases error variance in the norm data.
Simple simulations to help students visualize first lessons in probability: how and when binomial distribution approximates normal and how Law of big numbers and Central Limit Theorem work. Students can visualize correlations of different sizes and grasp important concepts when testing hypotheses.
learning statistics with jamovi1.0.1
This module provides examples data sets to accompany the book learning statistics with jamovi.
ufs: tools for confidence intervals and other tricks0.3.1
The ufs module makes functions from the eponymous R package available in jamovi. These include functions for computing confidence intervals for effect sizes, computing the required sample size for estimating an effect size with a confidence interval of a given width, producing diamond plots, creating a multiple response table, and to do some basic operations such as (dis)attenuate effect size estimates.
Tools for Behavior Change Researchers and Professionals0.2.2
This Jamovi module contains specialised analyses and visualisation tools for research in and application of behavior change science. These facilitate conducting determinant studies (for example, using confidence interval-based estimation of relevance, CIBER, or CIBERlite plots), systematically developing, reporting, and analysing interventions (for example, using acyclic behavior change diagrams or ABCDs), reporting about intervention effectiveness (using the Numbers Needed for Change), and computing the required sample size (using the Meaningful Change Definition). This package is especially useful for researchers in the field of behavior change or health psychology and to behavior change professionals such as intervention developers and prevention workers.
A comprehensive range of facilities to perform umbrella reviews with stratification of the evidence. This module accomplishes this aim by building on three core functions that: (i) automatically perform all required calculations in an umbrella review (including but not limited to pairwise meta-analyses), (ii) stratify evidence according to various classification criteria, and (iii) generate a visual representation of the results.
Randomize common experimental designs0.4.0
Randomize balanced single factor or full factorial experiments using Completely Randomized, Randomized Complete Block, or Latin Square designs. Generate plot maps, plot lists, and design property tables. Restore a particular randomization by seed. Prepare for randomization by making each factor name a column header in jamovi's spreadsheet, with levels of that factor listed below.
Descriptives Functions for Clinicopathological Research0.0.2.05
Descriptives Functions for Clinicopathological Research Descriptive functions from ClinicoPath jamovi module. ClinicoPath help researchers to generate natural language summaries of their dataset, generate cross tables with statistical tests, and survival analysis with survival tables, survival plots, and natural language summaries.
Multivariate Exploratory Data Analysis1.0.0
This module allows you to perform multivariate exploratory analyses the French way. In other words, you will be able to add supplementary information for a better understanding of your results. Three main methods have been implemented: Principal Component Analysis, Correspondence Analysis and Multiple Correspondence Analysis. You can also get an automatic description of a variable based on the other variables of the dataset (categorical/quantitative). Results are obtained thanks to the FactoMineR package. These analyses are described in the MEDA website.
Sensory Evaluation Data Analysis1.0.0
This module allows you to analyze two types of perception data. A first one, when stimuli are described according to a fixed list of sensory attributes (QDA data, for instance). A second one, when stimuli are described freely (Napping and sorting data). Results are obtained thanks to the SensoMineR package.
Bland-Altman Method Comparison0.5.1
This module provides Bland-Altman method comparison analysis, and is also available as an R package from CRAN
Parallel Use of Statistical Packages in Teaching0.1.3
When teaching statistics, it can often be desirable to uncouple the content from specific software packages. To ease such efforts, the Rosetta Stats book allows comparing analyses in different packages. This module is the companion to the Rosetta Stats book, containing the example datasets used in the book and eventually aiming to provide functions that produce output that is similar to output from other statistical packages, thereby facilitating 'software-agnostic' teaching of statistics.