Bayesian Longitudinal Regularized Quantile Mixed Model

With high-dimensional omics features, repeated measure ANOVA leads to longitudinal gene-environment interaction studies that have intra-cluster correlations, outlying observations and structured sparsity arising from the ANOVA design. In this package, we have developed robust sparse Bayesian mixed effect models tailored for the above studies (Fan et al. (2025) ). An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in 'C++'. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University.

Multiplicative Mixed Models using the Template Model Builder

Fit multiplicative mixed models using maximum likelihood estimation via the Template Model Builder (TMB), Kristensen K, Nielsen A, Berg CW, Skaug H, Bell BM (2016) . One version of the multiplicative mixed model is applied in Piepho (1999) . The package provides functions for calculating confidence intervals for the model parameters and for performing likelihood ratio tests.

Bayesian Model Selection for Generalized Linear Mixed Models

A Bayesian model selection approach for generalized linear mixed models. Currently, 'GLMMselect' can be used for Poisson GLMM and Bernoulli GLMM. 'GLMMselect' can select fixed effects and random effects simultaneously. Covariance structures for the random effects are a product of a unknown scalar and a known semi-positive definite matrix. 'GLMMselect' can be widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics. 'GLMMselect' is based on Xu, Ferreira, Porter, and Franck (202X), Bayesian Model Selection Method for Generalized Linear Mixed Models, Biometrics, under review.

Nonlinear Mixed Effects Models in Population PK/PD

Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 ). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 ).

Nonlinear Mixed Effects Models in Population PK/PD, Data

Datasets for 'nlmixr2' and 'rxode2'. 'nlmixr2' is used for fitting and comparing nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 ). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 ).

Analysis of Factorial Experiments

Convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated-measures), or mixed (i.e., split-plot) ANOVAs for data in long format (i.e., one observation per row), automatically aggregating multiple observations per individual and cell of the design. mixed() fits mixed models using lme4::lmer() and computes p-values for all fixed effects using either Kenward-Roger or Satterthwaite approximation for degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). afex_plot() provides a high-level interface for interaction or one-way plots using ggplot2, combining raw data and model estimates. afex uses type 3 sums of squares as default (imitating commercial statistical software).

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.

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 .