Note: -*- Encoding: utf-8 -*-
Package: stepmixr
Type: Package
Title: Interface to 'Python' Package 'StepMix'
Version: 0.1.3
Date: 2025-07-04
Authors@R: c(
   person("Éric", "Lacourse", role="aut"),
   person("Roxane", "de la Sablonnière", role="aut"),
   person("Charles-Édouard", "Giguère", role=c("aut", "cre"),
      email = "ce.giguere@gmail.com"),
   person("Sacha", "Morin", role="aut"),	
   person("Robin", "Legault", role="aut"),
   person("Félix", "Laliberté", role = "aut"),
   person("Zsusza", "Bakk", role="ctb") )   
Author: Éric Lacourse [aut],
  Roxane de la Sablonnière [aut],
  Charles-Édouard Giguère [aut, cre],
  Sacha Morin [aut],
  Robin Legault [aut],
  Félix Laliberté [aut],
  Zsusza Bakk [ctb]
Maintainer: Charles-Édouard Giguère <ce.giguere@gmail.com>
Depends: R (>= 4.0.0)
Imports: reticulate (>= 1.8)
Description: This is an interface for the 'Python' package
  'StepMix'. It is a 'Python' package following the scikit-learn API for
  model-based clustering and generalized mixture modeling (latent class/profile
  analysis) of continuous and categorical data. 'StepMix' handles missing values
  through Full Information Maximum Likelihood (FIML) and provides multiple stepwise
  Expectation-Maximization (EM) estimation methods based on pseudolikelihood
  theory. Additional features include support for covariates and distal outcomes,
  various simulation utilities, and non-parametric bootstrapping, which allows
  inference in semi-supervised and unsupervised settings. Software paper available
  at <doi:10.18637/jss.v113.i08>.
License: GPL-2
Encoding: UTF-8
LazyLoad: TRUE
URL: https://github.com/Labo-Lacourse/StepMixr
NeedsCompilation: no
Packaged: 2025-07-04 17:19:42 UTC; gigc2
Repository: CRAN
Date/Publication: 2025-07-04 21:40:06 UTC
Built: R 4.6.0; ; 2026-04-27 20:31:48 UTC; unix
