Automated Estimation of Sigmoidal and Piecewise Linear Mixed Models

Estimation of relatively complex nonlinear mixed-effects models, including the Sigmoidal Mixed Model and the Piecewise Linear Mixed Model with abrupt or smooth transition, through a single intuitive line of code and with automated generation of starting values.

Learning and Communicating Linear Mixed Models Without Data

Summarizes characteristics of linear mixed effects models without data or a fitted model by converting code for fitting lmer() from 'lme4' and lme() from 'nlme' into tables, equations, and visuals. Outputs can be used to learn how to fit linear mixed effects models in 'R' and to communicate about these models in presentations, manuscripts, and analysis plans.

Functional Linear Mixed Models for Densely Sampled Data

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

Power Analysis for Generalised Linear Mixed Models by Simulation

Calculate power for generalised linear mixed models, using simulation. Designed to work with models fit using the 'lme4' package. Described in Green and MacLeod, 2016 .

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) .