Package: ProfileGLMM
Type: Package
Title: Bayesian Profile Regression using Generalised Linear Mixed
        Models
Version: 1.0.2
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
NeedsCompilation: yes
Packaged: 2025-12-12 10:41:45 UTC; VNOB-0731
Author: Matteo Amestoy [aut, cre, cph],
  Mark van de Wiel [ths],
  Wessel van Wieringen [ths]
Maintainer: Matteo Amestoy <m.amestoy@amsterdamumc.nl>
Repository: CRAN
Date/Publication: 2025-12-18 13:50:07 UTC
Built: R 4.5.2; aarch64-apple-darwin20; 2025-12-19 08:59:39 UTC; unix
Archs: ProfileGLMM.so.dSYM
