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regMR: Regularized Finite Mixture Regression Models Using MM Algorithm

Provides a comprehensive framework for fitting regularized finite mixture regression models via an MM algorithm. The sparse group lasso (sgl) penalty is applied to parameter updates within the MM algorithm for variable selection with respect to groups and covariates. The package provides multiple functions for estimation and allows users to fit models over different lambda-alpha sgl penalties and group counts.

Version: 1.0.0
Depends: R (≥ 4.1.0)
Imports: furrr, future, ggplot2, graphics, mclust, purrr, Rcpp, reshape2, plotly, stats
LinkingTo: Rcpp, RcppArmadillo
Suggests: mvtnorm, rlang, testthat (≥ 3.0.0)
Published: 2026-07-31
DOI: 10.32614/CRAN.package.regMR (may not be active yet)
Author: Cameron Bechthold [aut, cre, cph], Vinay Joshy [aut, ctb, cph], Zeny Feng [aut, ths, cph], Grace Stelter [ctb]
Maintainer: Cameron Bechthold <cbechtho at uoguelph.ca>
BugReports: https://github.com/vjoshy/regMR/issues
License: MIT + file LICENSE
URL: https://github.com/vjoshy/regMR
NeedsCompilation: yes
Materials: README, NEWS
CRAN checks: regMR results

Documentation:

Reference manual: regMR.html , regMR.pdf

Downloads:

Package source: regMR_1.0.0.tar.gz
Windows binaries: r-devel: not available, r-release: regMR_1.0.0.zip, r-oldrel: not available
macOS binaries: r-release (arm64): regMR_1.0.0.tgz, r-oldrel (arm64): regMR_1.0.0.tgz, r-release (x86_64): regMR_1.0.0.tgz, r-oldrel (x86_64): regMR_1.0.0.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.