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: |
0.1.1 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
reticulate (≥ 1.8) |
| Published: |
2023-03-22 |
| Author: |
Éric Lacourse [aut],
Roxane de la Sablonnière [aut],
Charles-Édouard Giguère [aut, cre],
Sacha Morin [aut],
Robin Legault [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 |
| CRAN checks: |
stepmixr results |
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