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 |
| DOI: | 10.32614/CRAN.package.stepmixr |
| 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 |
| Reference manual: | stepmixr.pdf |
| 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-devel (arm64): stepmixr_0.1.2.tgz, r-release (arm64): stepmixr_0.1.2.tgz, r-oldrel (arm64): stepmixr_0.1.2.tgz, r-devel (x86_64): 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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