ProfileGLMM: 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) <doi:10.48550/arXiv.2510.08304>.

Version:	1.1.0
Depends:	R (≥ 3.5)
Imports:	Rcpp, LaplacesDemon, MCMCpack, Matrix, Spectrum, mvtnorm
LinkingTo:	Rcpp, RcppArmadillo, RcppDist
Suggests:	knitr, rmarkdown
Published:	2026-02-03
DOI:	10.32614/CRAN.package.ProfileGLMM
Author:	Matteo Amestoy [aut, cre, cph], Mark van de Wiel [ths], Wessel van Wieringen [ths]
Maintainer:	Matteo Amestoy <m.amestoy at amsterdamumc.nl>
BugReports:	https://github.com/MatteoAmestoy/ProfileGLMM-package/issues
License:	GPL-2
URL:	https://github.com/MatteoAmestoy/ProfileGLMM-package
NeedsCompilation:	yes
Materials:	README, NEWS
CRAN checks:	ProfileGLMM results

Documentation:

Reference manual:	ProfileGLMM.html , ProfileGLMM.pdf
Vignettes:	Introduction to ProfileGLMM (source, R code)

Downloads:

Package source:	ProfileGLMM_1.1.0.tar.gz
Windows binaries:	r-devel: ProfileGLMM_1.1.0.zip, r-release: ProfileGLMM_1.1.0.zip, r-oldrel: ProfileGLMM_1.1.0.zip
macOS binaries:	r-release (arm64): ProfileGLMM_1.1.0.tgz, r-oldrel (arm64): ProfileGLMM_1.1.0.tgz, r-release (x86_64): ProfileGLMM_1.1.0.tgz, r-oldrel (x86_64): ProfileGLMM_1.1.0.tgz
Old sources:	ProfileGLMM archive

Linking:

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