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stepmixr  

Interface to 'Python' Package 'StepMix'
View on CRAN: Click here


Download and install stepmixr package within the R console
Install from CRAN:
install.packages("stepmixr")

Install from Github:
library("remotes")
install_github("cran/stepmixr")

Install by package version:
library("remotes")
install_version("stepmixr", "0.1.3")



Attach the package and use:
library("stepmixr")
Maintained by
Charles-Édouard Giguère
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-11-18
Latest Update: 2025-07-04
Description:
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.
How to cite:
Charles-Édouard Giguère (2022). stepmixr: Interface to 'Python' Package 'StepMix'. R package version 0.1.3, https://cran.r-project.org/web/packages/stepmixr. Accessed 05 Mar. 2026.
Previous versions and publish date:
0.1.0 (2022-11-18 11:00), 0.1.1 (2023-03-22 17:50), 0.1.2 (2024-01-09 23:20)
Other packages that cited stepmixr R package
View stepmixr citation profile
Other R packages that stepmixr depends, imports, suggests or enhances
Complete documentation for stepmixr
Functions, R codes and Examples using the stepmixr R package
Some associated functions: Datasets . bootstrap . bootstrap_stats . fit . install.stepmix . mixed_descriptor . predict . savefit . stepmix . 
Some associated R codes: Datasets.R . stepmix.R .  Full stepmixr package functions and examples
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