generalized-linear-mixed-models

Covers the basics of mixed models, mostly using @lme4

GLMMs with adaptive Gaussian quadrature

Specification language for generating Generalized Linear Models (with or without mixed effects) from conceptual models

Code for the paper: Mixed Models with Multiple Instance Learning

Workshop 7 - General and generalized linear mixed models (LMM and GLMM)

R package with quasi-Monte Carlo methods to estimate mixed models commonly used for random effect structures from pedigrees.

Reference implementations for (generalized) linear mixed models.

The Julia package for estimating and testing a generalized linear mixed model with normal mixture random effects

This repository contains data, scripts, and figures for the manuscript entitled 'A fast spectral recovery does not necessarily indicate post-fire forest recovery' which compares spectral recovery, topographic and climatic data, and field measurements of post-fire vegetation dynamics in the Blue Mountains, OR. For more information, see the README

Materials for a 3 to 4 hour workshop on Bayesian Statistics using the R package `brms`

The aim of the GLMM Project is to build general linear (mixed) models that predicts the total number of medals won by countries. GLMs and GLMMs are examined in the Part 1 and Part 2 respectively. Refer to memos in pdf files for details.

Repository for code and data for Basham et al 2022 (Oceologia, https://doi.org/10.1007/s00442-022-05108-9)

Reproducible R Markdown analyses of eye-tracking attention bias data using GLMMS to model heterogeneous distributions and participant-specific task effects.

Analysis of the evolution of prevalence of thought disorders

A Study of the Risk Factors for Leptospirosis Infection in Kenya.

Drug effective check

Consultancy case with the intent to analyse work conditions for civil aviation.