Bayesian Model-Based Clustering with Sparse Conditional Mixture Models R package scmix version 0.1.1 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. Software Depends: R (>= 4.1) Imports: graphics, stats Suggests: knitr, mclust, rmarkdown, testthat (>= 3.0.0) Aqi Dong <donga2@erau.edu> Comprehensive R Archive Network (CRAN) Aqi Dong [aut, cre], Yang-Li Liao [aut], Danhyang Lee [aut] GPL (>= 3) 2026-08-20 application/tgz https://CRAN.R-project.org/package=scmix doi:10.32614/CRAN.package.scmix