mixed-models

💪 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. Now almost entirely superseded by the models-by-example repo.

An R package for experimental psychologists

All the convenience of lme4 in python

Covers the basics of mixed models, mostly using @lme4

Material for a workshop on Bayesian stats with R

👓 Functions related to R visualizations

GLMMs with adaptive Gaussian quadrature

Bayesian estimation of the finishing skill of football players

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

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

Extended Joint Models for Longitudinal and Survival 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.

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

Workshop on using Mixed Models with R

Formulas for mixed-effects models in Python

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.

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

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

🎓 Tidy multilevel modeling tools for academics

Notebooks for SMLP2021

Functions for using mgcv for mixed models. 📈

Demonstration of alternatives to lme4

Code used to carry out parameter estimation, correlation estimation, type 1 error analysis, and power analysis for our "Pseudoreplication in Single-Cell" study

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