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mixed-models

easystats/performance
https://static.github-zh.com/github_avatars/easystats?size=40

#计算机科学#💪 Models' quality and performance metrics (R2, ICC, LOO, AIC, BF, ...)

R 1.1 k
15 天前
https://static.github-zh.com/github_avatars/JuliaStats?size=40

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

Julia 433
5 天前
https://static.github-zh.com/github_avatars/ejolly?size=40

All the convenience of lme4 in python

Python 210
11 天前
https://static.github-zh.com/github_avatars/m-clark?size=40

#计算机科学#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 **Superseded by the models-by-example repo**.

R 196
5 年前
https://static.github-zh.com/github_avatars/strengejacke?size=40

Effect size measures and significance tests

R 191
3 个月前
https://static.github-zh.com/github_avatars/m-clark?size=40

Covers the basics of mixed models, mostly using @lme4

R 143
4 年前
https://static.github-zh.com/github_avatars/drizopoulos?size=40

Extended Joint Models for Longitudinal and Survival Data

R 91
4 天前
https://static.github-zh.com/github_avatars/oliviergimenez?size=40
HTML 74
1 年前
https://static.github-zh.com/github_avatars/m-clark?size=40

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

R 70
3 年前
https://static.github-zh.com/github_avatars/m-clark?size=40

👓 Functions related to R visualizations

R 63
5 年前
https://static.github-zh.com/github_avatars/bambinos?size=40

Formulas for mixed-effects models in Python

Python 63
8 个月前
https://static.github-zh.com/github_avatars/drizopoulos?size=40

GLMMs with adaptive Gaussian quadrature

R 62
6 个月前
https://static.github-zh.com/github_avatars/unfoldtoolbox?size=40

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

Julia 62
7 天前
https://static.github-zh.com/github_avatars/huffyhenry?size=40
R 40
8 年前
https://static.github-zh.com/github_avatars/strengejacke?size=40

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.

R 37
2 年前
https://static.github-zh.com/github_avatars/randel?size=40

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

R 35
8 年前
https://static.github-zh.com/github_avatars/cran-task-views?size=40

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

R 28
2 个月前
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