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

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 **Superseded by the models-by-example repo**.

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

Covers the basics of mixed models, mostly using @lme4

An R package for experimental psychologists

All the convenience of lme4 in python

Extended Joint Models for Longitudinal and Survival Data

Material for a workshop on Bayesian stats with R

Effect size measures and significance tests

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, J…

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

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

Formulas for mixed-effects models in Python

GLMMs with adaptive Gaussian quadrature

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

Gene-level general linear mixed model

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

CRAN Task View: Mixed, Multilevel, and Hierarchical Models in R