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

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

All the convenience of lme4 in python

Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms. **Superseded by the models-by-example repo**.

Effect size measures and significance tests

An R package for experimental psychologists

Covers the basics of mixed models, mostly using @lme4

Extended Joint Models for Longitudinal and Survival Data

Material for a workshop on Bayesian stats with R

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

By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.

👓 Functions related to R visualizations

GLMMs with adaptive Gaussian quadrature

Formulas for mixed-effects models in Python

Neuroimaging (EEG, fMRI, pupil ...) regression analysis in Julia

Bayesian estimation of the finishing skill of football players

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.

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

Julia package for fitting mixed-effects models with flexible random/repeated covariance structure.