mixed-models

JuliaStats/MixedModels.jl

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

ejolly/pymer4

All the convenience of lme4 in python

m-clark/Miscellaneous-R-Code

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 **Superseded by the models-by-example repo**.

strengejacke/sjstats

Effect size measures and significance tests

neuropsychology/psycho.R

An R package for experimental psychologists

m-clark/mixed-models-with-R

Covers the basics of mixed models, mostly using @lme4

drizopoulos/JMbayes2

Extended Joint Models for Longitudinal and Survival Data

oliviergimenez/bayesian-stats-with-R

Material for a workshop on Bayesian stats with R

m-clark/mixedup

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

m-clark/models-by-example

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

m-clark/visibly

👓 Functions related to R visualizations

drizopoulos/GLMMadaptive

GLMMs with adaptive Gaussian quadrature

bambinos/formulae

Formulas for mixed-effects models in Python

unfoldtoolbox/Unfold.jl

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

covaruber/sommer

huffyhenry/statsbomb-bayesian-shooting

Bayesian estimation of the finishing skill of football players

strengejacke/mixed-models-snippets

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.

randel/MixRF

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

cran-task-views/MixedModels

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

m-clark/generalized-additive-models-workshop-2019

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

RePsychLing/SMLP2021

Notebooks for SMLP2021

PharmCat/Metida.jl

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

m-clark/mixed-models-with-r-workshop-2019

Workshop on using Mixed Models with R

timeu/PyGWAS

Mouse-Imaging-Centre/RMINC

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:

JuliaMixedModels/EmbraceUncertainty

The book "Embrace Uncertainty: Fitting Mixed-Effects Models with Julia"

kdzimm/PseudoreplicationPaper

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

myles-lewis/glmmSeq

Gene-level general linear mixed model

AIH-SGML/mixmil

Code for the paper: Mixed Models with Multiple Instance Learning

matsueushi/kubo_analysis_julia

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

RePsychLing/MixedModelsSim.jl

Simulation tools for Mixed Models

briencj/asremlPlus

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

mkearney/tidymlm

🎓 Tidy multilevel modeling tools for academics

SchmidtPaul/MMFAIR

:chart_with_upwards_trend::seedling: Mixed Models for Agriculture in R

m-clark/mixed-models-with-R-workshop

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