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.

Longitudinal and multilevel statistical modeling (GEE & GLMM) on Brazilian E-Commerce records using PostgreSQL and R. Investigates population repurchase dynamics, logistics friction, unobserved heterogeneity, and merchant overdispersion.

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

Project for generalized linear models class at BYU. Modeling the probability that a pitch is a strike using generalized additive models and determining catcher framing abilities and umpire influence on the strike zone using generalized linear mixed models.

Here you can find a data analysis project based on my dissertation that demonstrates my skills in R programming, as well as statistical data analysis and data visualization

This study used the data collected from Lehigh University's peer tutoring program which focused on 20 courses involving problem-solving skills over three academic years, 2003-04 through 2005-06. An OLS model and generalized liner mixed-effects models were used to measure effect of participation in tutoring in terms of hours spent getting tutored…

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

Code related to my PhD's work