Package: ProfileGLMM 1.1.0

Matteo Amestoy

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>.

Authors:Matteo Amestoy [aut, cre, cph], Mark van de Wiel [ths], Wessel van Wieringen [ths]

ProfileGLMM_1.1.0.tar.gz
ProfileGLMM_1.1.0.zip(r-4.7)ProfileGLMM_1.1.0.zip(r-4.6)ProfileGLMM_1.1.0.zip(r-4.5)
ProfileGLMM_1.1.0.tgz(r-4.6-x86_64)ProfileGLMM_1.1.0.tgz(r-4.6-arm64)ProfileGLMM_1.1.0.tgz(r-4.5-x86_64)ProfileGLMM_1.1.0.tgz(r-4.5-arm64)
ProfileGLMM_1.1.0.tar.gz(r-4.7-arm64)ProfileGLMM_1.1.0.tar.gz(r-4.7-x86_64)ProfileGLMM_1.1.0.tar.gz(r-4.6-arm64)ProfileGLMM_1.1.0.tar.gz(r-4.6-x86_64)
ProfileGLMM_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ProfileGLMM/json (API)

# Install 'ProfileGLMM' in R:

install.packages('ProfileGLMM', repos = c('https://matteoamestoy.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/matteoamestoy/profileglmm-package/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

examp - List of the different outputs of the main functions for the examples
exposure_data - Simulated Data and Parameters for a exposure profile linear mixed model
piecewise_data - Simulated Data and Parameters for a Piecewise Example

On CRAN:

openblas cpp openmp

4.78 score 256 downloads 6 exports 39 dependencies

Last updated from:2fc3c9e48a. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	194
linux-devel-x86_64	OK	185
source / vignettes	OK	394
linux-release-arm64	OK	203
linux-release-x86_64	OK	197
macos-release-arm64	OK	127
macos-release-x86_64	OK	510
macos-oldrel-arm64	OK	144
macos-oldrel-x86_64	OK	323
windows-devel	OK	199
windows-release	OK	188
windows-oldrel	OK	206
wasm-release	OK	197

Exports:encodeCat prior_init profileGLMM_Gibbs profileGLMM_postProcess profileGLMM_preprocess theta_init

Dependencies:cli ClusterR coda cpp11 diptest farver ggplot2 glue gmp gtable isoband labeling LaplacesDemon lattice lifecycle MASS Matrix MatrixModels mcmc MCMCpack mvtnorm quantreg R6 RColorBrewer Rcpp RcppArmadillo RcppDist RcppParallel Rfast rlang S7 scales SparseM Spectrum survival vctrs viridisLite withr zigg

Introduction to ProfileGLMM

Introduction | Model specifications | Posterior sampling of the parameters and postprocessing of the MCMC chain

Rendered fromIntro_to_ProfileGLMM.Rmdusingknitr::rmarkdown

Last update: 2026-02-02
Started: 2026-01-28

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
One-Hot Encodes Factor Variables (FIRST Level as Reference)	encodeCat
List of the different outputs of the main functions for the examples	examp
Simulated Data and Parameters for a exposure profile linear mixed model	exposure_data
Simulated Data and Parameters for a Piecewise Example	piecewise_data
Plot method for pglmm_fit continuous covariates cluster characteristics	plot.pglmm_fit
Prediction of cluster memberships and outcomes	predict.pglmm_fit
Print method for pglmm_data	print.pglmm_data
Print method for pglmm_fit	print.pglmm_fit
Print method for pglmm_mcmc	print.pglmm_mcmc
Initialize the prior hyperparameters for the Profile GLMM	prior_init
R Wrapper for Profile GLMM Gibbs Sampler (C++ backend)	profileGLMM_Gibbs
Post-process MCMC Output for Profile GLMM	profileGLMM_postProcess
Preprocess the data from a list describing the profile LMM model	profileGLMM_preprocess
Print method for pglmm_fit	summary.pglmm_fit
Initialize the variables for the Gibbs sampler chain	theta_init