Functional Linear Mixed Models for Densely Sampled Data

Estimation of functional linear mixed models for densely sampled data based on functional principal component analysis.

Data Sets from "SAS System for Mixed Models

Data sets and sample lmer analyses corresponding to the examples in Littell, Milliken, Stroup and Wolfinger (1996), "SAS System for Mixed Models", SAS Institute.

Linear Mixed Models - An Introduction with Applications in Veterinary Research

R Codes and Datasets for Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998). Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.

Scale Mixture of Skew-Normal Linear Mixed Models

It fits scale mixture of skew-normal linear mixed models using either an expectation–maximization (EM) type algorithm or its accelerated version (Damped Anderson Acceleration with Epsilon Monotonicity, DAAREM), including some possibilities for modeling the within-subject dependence .

Post-Estimation Functions for Generalized Linear Mixed Models

Several functions for working with mixed effects regression models for limited dependent variables. The functions facilitate post-estimation of model predictions or margins, and comparisons between model predictions for assessing or probing moderation. Additional helper functions facilitate model comparisons and implements simulation-based inference for model predictions of alternative-specific outcome models. See also, Melamed and Doan (2024, ISBN: 978-1032509518).

Fast Fitting of Stable Isotope Mixing Models with Covariates

Fast fitting of Stable Isotope Mixing Models in R. Allows for the inclusion of covariates. Also has built-in summary functions and plot functions which allow for the creation of isospace plots. Variational Bayes is used to fit these models, methods as described in: Tran et al., (2021) .

Maximum Likelihood Estimation for Generalized Linear Mixed Models

Maximum likelihood estimation for generalized linear mixed models via Monte Carlo EM. For a description of the algorithm see Brian S. Caffo, Wolfgang Jank and Galin L. Jones (2005) .

Mixed Model Association Test for GEne-Environment Interaction

Use a 'glmmkin' class object (GMMAT package) from the null model to perform generalized linear mixed model-based single-variant and variant set main effect tests, gene-environment interaction tests, and joint tests for association, as proposed in Wang et al. (2020) .

Reproducible Ecological Generalized Linear Mixed Model Pipelines

Fits and compares generalized linear mixed models for multiple ecological responses and environmental predictors. The package supports additive and temporal-interaction candidate models, AICc model selection, likelihood-ratio tests, coefficient extraction, Nakagawa R-squared, simulation-based diagnostics, figures, and spreadsheet exports. Model selection follows Burnham and Anderson (2002, ISBN:9780387953649); marginal and conditional R-squared follow Nakagawa and Schielzeth (2013) .

Estimate (Generalized) Linear Mixed Models with Factor Structures

Utilizes the 'lme4' and 'optimx' packages (previously the optim() function from 'stats') to estimate (generalized) linear mixed models (GLMM) with factor structures using a profile likelihood approach, as outlined in Jeon and Rabe-Hesketh (2012) and Rockwood and Jeon (2019) . Factor analysis and item response models can be extended to allow for an arbitrary number of nested and crossed random effects, making it useful for multilevel and cross-classified models.