Package: ProfileGLMM
Type: Package
Title: Bayesian Profile Regression using Generalised Linear Mixed
        Models
Version: 1.1.0
Authors@R: c(
    person(
      "Matteo", "Amestoy",
      email = "m.amestoy@amsterdamumc.nl",
      role = c("aut", "cre", "cph")
    ),
    person(
      "Mark", "van de Wiel",
      email = "mark.vdwiel@amsterdamumc.nl",
      role = c("ths")
    ),
    person(
      "Wessel", "van Wieringen",
      email = "w.vanwieringen@amsterdamumc.nl",
      role = c("ths")
    )
  )
Description: 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>.
License: GPL-2
Encoding: UTF-8
LazyData: true
LazyDataCompression: xz
RoxygenNote: 7.3.2
LinkingTo: Rcpp, RcppArmadillo, RcppDist
Imports: Rcpp, LaplacesDemon, MCMCpack, Matrix, Spectrum, mvtnorm
Depends: R (>= 3.5)
URL: https://github.com/MatteoAmestoy/ProfileGLMM-package
BugReports: https://github.com/MatteoAmestoy/ProfileGLMM-package/issues
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-10-22 10:06:51 UTC; root
Author: Matteo Amestoy [aut, cre, cph], Mark van de Wiel [ths], Wessel
        van Wieringen [ths]
Maintainer: Matteo Amestoy <m.amestoy@amsterdamumc.nl>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-02-03 12:00:17 UTC
RemoteUrl: https://github.com/cran/ProfileGLMM
RemoteRef: HEAD
RemoteSha: ef380a56413a855978a43053c1e4514467d5aa1a