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scmix  

Bayesian Model-Based Clustering with Sparse Conditional Mixture Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("scmix", "0.1.1")



Attach the package and use:
library("scmix")
Maintained by
Aqi Dong
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-08-20
Latest Update: 2026-08-20
Description:
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.
How to cite:
Aqi Dong (2026). scmix: Bayesian Model-Based Clustering with Sparse Conditional Mixture Models. R package version 0.1.1, https://cran.r-project.org/web/packages/scmix. Accessed 24 Oct. 2026.
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