stepmixr: Interface to 'Python' Package 'StepMix'

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.

Version: 0.1.2
Depends: R (≥ 4.0.0)
Imports: reticulate (≥ 1.8)
Published: 2024-01-09
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 at>
License: GPL-2
NeedsCompilation: no
In views: Cluster
CRAN checks: stepmixr results


Reference manual: stepmixr.pdf


Package source: stepmixr_0.1.2.tar.gz
Windows binaries: r-prerel:, r-release:, r-oldrel:
macOS binaries: r-prerel (arm64): stepmixr_0.1.2.tgz, r-release (arm64): stepmixr_0.1.2.tgz, r-oldrel (arm64): stepmixr_0.1.2.tgz, r-prerel (x86_64): stepmixr_0.1.2.tgz, r-release (x86_64): stepmixr_0.1.2.tgz
Old sources: stepmixr archive


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