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message: 'To cite package "mixturemodelsr" in publications use:'
type: software
license: MIT
title: 'mixturemodelsr: R Wrapper for the Mixture-Models Python Library'
version: 0.1.20
doi: 10.32614/CRAN.package.mixturemodelsr
abstract: 'First R package enabling mixture models for high-dimensional data through
  gradient-based optimization with Automatic Differentiation (AD). Provides an R interface
  to the ''Mixture-Models'' Python package (Kasa et al., 2024) via reticulate. Unlike
  traditional EM-based approaches (e.g., mclust, flexmix), this package uses AD and
  gradient-based optimization (including second-order Newton-CG) to fit Gaussian Mixture
  Models (GMM), Mixture of Factor Analyzers (MFA), Parsimonious GMM (PGMM), MCLUST
  family, and t-mixture models without requiring stringent modeling constraints, making
  it suitable for high-dimensional settings where the number of parameters exceeds
  the sample size. Reference: Kasa, S. R., Yijie, H., Kasa, S. K., & Rajan, V. (2024).
  Mixture-Models: a one-stop Python Library for Model-based Clustering using various
  Mixture Models. arXiv preprint arXiv:2402.10229.'
authors:
- family-names: Kasa
  given-names: Siva Rajesh
  email: mixturemodelscollab@gmail.com
repository: https://kasakh.r-universe.dev
repository-code: https://github.com/kasakh/mixturemodelsr
commit: bf21e3d062d01e7f854d7f9197eec0d6e559147d
url: https://github.com/kasakh/Mixture-Models
date-released: '2026-01-01'
contact:
- family-names: Kasa
  given-names: Siva Rajesh
  email: mixturemodelscollab@gmail.com