Interface to 'Python' Package 'StepMix'
Non-parametric bootstrap of StepMix estimator.
Non-parametric boostrap of StepMix estimator.
Series of function to simulate data.
Fit a mixture using the stepmix python package.
Install stepmix python package into python via reticulate.
Utility function for mixture using mixed description.
Predict the membership (probabilities) using the fit of the stepmix py...
Save the fit of a mixture using the stepmix python package.
R interface to stepmix in StepMix python.
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.