Package: mixturemodelsr Type: Package Title: R Wrapper for the Mixture-Models Python Library Version: 0.1.20 Authors@R: person("Siva Rajesh", "Kasa", email = "mixturemodelscollab@gmail.com", role = c("aut", "cre", "cph")) Description: 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. License: MIT + file LICENSE Encoding: UTF-8 Depends: R (>= 3.6.0) Imports: reticulate (>= 1.14) Suggests: testthat (>= 3.0.0), knitr, rmarkdown SystemRequirements: Python (>= 3.6), Python package 'mixture-models' (available on PyPI) URL: https://github.com/kasakh/mixturemodelsr, https://github.com/kasakh/Mixture-Models BugReports: https://github.com/kasakh/mixturemodelsr/issues Roxygen: list(markdown = TRUE) RoxygenNote: 7.3.3 VignetteBuilder: knitr Config/testthat/edition: 3 Config/pak/sysreqs: libpng-dev python3 Repository: https://kasakh.r-universe.dev Date/Publication: 2026-01-01 00:33:16 UTC RemoteUrl: https://github.com/kasakh/mixturemodelsr RemoteRef: HEAD RemoteSha: bf21e3d062d01e7f854d7f9197eec0d6e559147d NeedsCompilation: no Packaged: 2026-10-10 14:16:22 UTC; root Author: Siva Rajesh Kasa [aut, cre, cph] Maintainer: Siva Rajesh Kasa