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
Config/pak/sysreqs: libgmp3-dev make
Repository: https://matteoamestoy.r-universe.dev
Date/Publication: 2026-02-03 10:58:21 UTC
RemoteUrl: https://github.com/matteoamestoy/profileglmm-package
RemoteRef: HEAD
RemoteSha: 2fc3c9e48abf07147a1d5461dbac626be44753db
NeedsCompilation: yes
Packaged: 2026-07-04 06:19:59 UTC; root
Author: Matteo Amestoy [aut, cre, cph],
  Mark van de Wiel [ths],
  Wessel van Wieringen [ths]
Maintainer: Matteo Amestoy <m.amestoy@amsterdamumc.nl>