scmix: Bayesian Model-Based Clustering with Sparse Conditional Mixture Models

Fits Bayesian sparse conditional (Gaussian) mixture models for model-based clustering. Each mixture component factorizes into a chain of univariate polynomial regressions with per-component, per-equation Bayesian variable selection under a centered Zellner g-prior; the number of clusters is selected within a single run via an overfitted sparse mixture (Dirichlet concentration 1/K). The blocked Gibbs sampler draws the selection sets exactly by enumeration (or by validated single-flip Metropolis-Hastings in higher dimension), is provably well-posed under a documented proper fallback prior, and reports a label-invariant consensus partition (Dahl's least-squares criterion). Companion package to Dong, Liao, and Lee (2026), "Replacing three nested searches with one sweep: a Bayesian treatment of sparse conditional mixture clustering". Multiple-imputation functionality for the same engine is also exposed.

Version:	0.1.1
Depends:	R (≥ 4.1)
Imports:	graphics, stats
Suggests:	knitr, mclust, rmarkdown, testthat (≥ 3.0.0)
Published:	2026-08-20
DOI:	10.32614/CRAN.package.scmix
Author:	Aqi Dong [aut, cre], Yang-Li Liao [aut], Danhyang Lee [aut]
Maintainer:	Aqi Dong <donga2 at erau.edu>
License:	GPL (≥ 3)
NeedsCompilation:	no
CRAN checks:	scmix results

Documentation:

Reference manual:	scmix.html , scmix.pdf
Vignettes:	scmix: Bayesian sparse conditional mixture clustering (source, R code)

Downloads:

Package source:	scmix_0.1.1.tar.gz
Windows binaries:	r-devel: not available, r-release: scmix_0.1.1.zip, r-oldrel: scmix_0.1.1.zip
macOS binaries:	r-release (arm64): scmix_0.1.1.tgz, r-oldrel (arm64): scmix_0.1.1.tgz, r-release (x86_64): scmix_0.1.1.tgz, r-oldrel (x86_64): scmix_0.1.1.tgz

Linking:

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