Enhancing `ggplot2` plots with statistical analysis 📊🎨📣

💪 Models' quality and performance metrics (R2, ICC, LOO, AIC, BF, ...)

A Julia package for fitting (statistical) mixed-effects models

Statistical Functions for Regression Models

Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms.

An R package for experimental psychologists

All the convenience of lme4 in python

👓 Functions related to R visualizations

Covers the basics of mixed models, mostly using @lme4

Bayesian estimation of the finishing skill of football players

GLMMs with adaptive Gaussian quadrature

A random-forest-based approach for imputing clustered incomplete data

This repository collects various small code snippets or short instructions on how to use or define specific mixed models, mostly with packages lme4 and glmmTMB.

Workshop on using Mixed Models with R

Statistics for MINC volumes: A library to integrate voxel-based statistics for MINC volumes into the R environment. Supports getting and writing of MINC volumes, running voxel-wise linear models, correlations, etc.; correcting for multiple comparisons using the False Discovery Rate, and more. With contributions from Jason Lerch, Chris Hammill, Jim Nikelski and Matthijs van Eede. Some additional information can be found here:

A workshop on using generalized additive models and the mgcv package.

🎓 Tidy multilevel modeling tools for academics

Demonstration of alternatives to lme4

Documents that go into methodological detail regarding various statistical procedures.

asremlPlus is an R package that augments the use of 'ASReml-R' and 'ASReml4-R' in fitting mixed models

Functions for using mgcv for mixed models. 📈

multinomial random effects

An R package for extracting results from mixed models that are easy to use and viable for presentation.

A quick reference for how to run many models in R.

This is the companion slides, data, and RStudio project for a workshop on mixed models.

Introduction to rstanarm

「データ解析のための統計モデリング入門」のJulia版Jupyter Notebook

glmmplus is an R package based on the notion that missing data is a fundamental part of nearly any analysis and that the flagship procedures that allow fitting of linear models and variable selection should include better options than ejecting rows for dealing with missing values.

Second year of the workshop series on biological statistics in R at RSB, ANU.