Variable Selection in Linear Mixed Models for SNP Data

Fit penalized multivariable linear mixed models with a single random effect to control for population structure in genetic association studies. The goal is to simultaneously fit many genetic variants at the same time, in order to select markers that are independently associated with the response. Can also handle prior annotation information, for example, rare variants, in the form of variable weights. For more information, see the website below and the accompanying paper: Bhatnagar et al., "Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models", 2020, .

Tables and Graphs for Mixed Models for Repeated Measures (MMRM)

Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see for example Cnaan, Laird and Slasor (1997) . This package provides an interface for fitting MMRM within the 'tern' < https://cran.r-project.org/package=tern> framework by Zhu et al. (2023) and tabulate results easily using 'rtables' < https://cran.r-project.org/package=rtables> by Becker et al. (2023). It builds on 'mmrm' < https://cran.r-project.org/package=mmrm> by Sabanés Bové et al. (2023) for the actual MMRM computations.

Bayesian Spatial and Space-Time Linear Mixed Models

Fits Bayesian linear mixed models for spatial and space-time data with fixed effects, independent and identically distributed (iid) grouped random effects, and structured latent processes. The formula interface supports first-order autoregressive (AR(1)) effects, dense Gaussian processes, nearest-neighbor Gaussian processes, proper and Leroux conditional autoregressive (CAR) effects, ordered directed acyclic graph autoregressive (DAGAR) effects, separable CAR-time and DAGAR-time effects, and spatially varying coefficients. The sampler uses sparse precision matrix calculations when available and includes post-fitting tools for latent process recovery, fitted values, prediction, pointwise log likelihoods, and posterior sample extraction. Method details include Datta et al. (2016) , Finley et al. (2019) , Datta et al. (2019) , and May and Finley (2025) .

Bayesian Robust Generalized Mixed Models for Longitudinal Data

To perform model estimation using MCMC algorithms with Bayesian methods for incomplete longitudinal studies on binary and ordinal outcomes that are measured repeatedly on subjects over time with drop-outs. Details about the method can be found in the vignette or < https://sites.google.com/view/kuojunglee/r-packages/bayesrgmm>.

Fit a Cosinor Model Using a Generalized Mixed Modeling Framework

Allows users to fit a cosinor model using the 'glmmTMB' framework. This extends on existing cosinor modeling packages, including 'cosinor' and 'circacompare', by including a wide range of available link functions and the capability to fit mixed models. The cosinor model is described by Cornelissen (2014) .

Generalized Linear Mixed Models using Template Model Builder

Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation.

Partial Eta-Squared for Crossed, Nested, and Mixed Linear Mixed Models

Computes partial eta-squared effect sizes for fixed effects in linear mixed models fitted with the 'lme4' package. Supports crossed, nested, and mixed (crossed-and-nested) random effects structures with any number of grouping factors. Mixed designs handle cases where grouping factors are simultaneously crossed with some variables and nested within others (e.g., photos nested within models, but both crossed with participants). Factor predictors are supported directly, and a single factor-level (omnibus) effect size can be obtained for a multi-level factor or multi-df interaction. Random slope variances are translated to the outcome scale using a variance decomposition approach, correctly accounting for predictor scaling and interaction terms. Both general and operative effect sizes are provided, with optional parametric bootstrap confidence intervals. For correlated predictors, per-predictor effect sizes use unique (semipartial) variance by default. Methods are based on Correll, Mellinger, McClelland, and Judd (2020) , Correll, Mellinger, and Pedersen (2022) , and Rights and Sterba (2019) .

Bayesian Profile Regression using Generalised Linear Mixed Models

Implements a Bayesian profile regression using a generalized linear mixed model as output model. The package allows for binary (probit mixed model) and continuous (linear mixed model) outcomes and both continuous and categorical clustering variables. The package utilizes 'RcppArmadillo' and 'RcppDist' for high-performance statistical computing in C++. For more details see Amestoy & al. (2025) .

Generalized Additive Mixed Model Analysis via Slice Sampling

Uses a slice sampling-based Markov chain Monte Carlo to conduct Bayesian fitting and inference for generalized additive mixed models. Generalized linear mixed models and generalized additive models are also handled as special cases of generalized additive mixed models. The methodology and software is described in Pham, T.H. and Wand, M.P. (2018). Australian and New Zealand Journal of Statistics, 60, 279-330 .

Generalized Linear Mixed Model Analysis via Expectation Propagation

Approximate frequentist inference for generalized linear mixed model analysis with expectation propagation used to circumvent the need for multivariate integration. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. The methodology is described in Hall, Johnstone, Ormerod, Wand and Yu (Journal of the American Statistical Association, 2020).