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 gmail.com>
License:	GPL-2
URL:	https://github.com/Labo-Lacourse/StepMixr
NeedsCompilation:	no
In views:	Cluster
CRAN checks:	stepmixr results

Documentation:

Reference manual:

stepmixr.pdf

Downloads:

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

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