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.
- Version0.1.2
- R versionunknown
- LicenseGPL-2
- Needs compilation?No
- Last release01/09/2024
Documentation
Team
Charles-Édouard Giguère
Éric Lacourse
Show author detailsRolesAuthorRoxane de la Sablonnière
Show author detailsRolesAuthorSacha Morin
Show author detailsRolesAuthorRobin Legault
Show author detailsRolesAuthorFélix Laliberté
Show author detailsRolesAuthorZsusza Bakk
Show author detailsRolesContributor
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- Imports1 package