GAMLj: GLM, Mixed, Generalized and Generalized mixed models for jamovi
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Updated
Sep 4, 2024 - R
GAMLj: GLM, Mixed, Generalized and Generalized mixed models for jamovi
Estimation Statistics with Confidence Intervals
ClinicoPath jamovi Module
This module is a tool for calculating correlations such as Partial, Tetrachoric, Intraclass correlation coefficients, Bootstrap agreement, Rater reliability, Generalizability Theory, Analytic Hierarchy Process, and allows users to produce Gaussian Graphical Model and Partial plot.
This module includes Item Statistics, Model fit, Differential Item Functioning, Wright Map, Expected Scores Curve,and Item Characteristic Curve for DIF using MML estimation of the Rasch measurement model. Furthermore you can analyze DIF, Distractor analysis and Many facet Rasch model.
wrapper functions to use ggstatsplot functions as a module in jamovi
Latent Class analysis: This module allows users to conduct LCA, Multiple group LCA, and Multilevel LCA based on glca R package, and provide plot such as Profile plot and Radar chart within module.
This module allows users to analyze k-means & hierarchical clustering, and visualize results of Principal Component, Correspondence Analysis, Discriminant analysis, Decision tree, Multidimensional scaling, Multiple Factor Analysis, Machine learning, and Prophet analysis.
Latent Class Analysis(LCA), LCA for ordinal indicators, Latent class growth modeling, Laten Profile Analysis, Rasch model, Linear Logistic Test Model, Rasch mixture model, linear and equipercentile equating can be performed within module.
A manual that provides a brief step-by-step instructions on data analysis using R and Jamovi.
Create scatter plots with marginal density / box plots in jamovi and R
Add a description, image, and links to the jamovi topic page so that developers can more easily learn about it.
To associate your repository with the jamovi topic, visit your repo's landing page and select "manage topics."