{ "_id": "6a1c0f581d7bb097a0a2e1f7", "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\",\nrole = c(\"aut\", \"cre\", \"cph\"))", "Description": "First R package enabling mixture models for\nhigh-dimensional data through gradient-based optimization with\nAutomatic Differentiation (AD). Provides an R interface to the\n'Mixture-Models' Python package (Kasa et al., 2024) via\nreticulate. Unlike traditional EM-based approaches (e.g.,\nmclust, flexmix), this package uses AD and gradient-based\noptimization (including second-order Newton-CG) to fit Gaussian\nMixture Models (GMM), Mixture of Factor Analyzers (MFA),\nParsimonious GMM (PGMM), MCLUST family, and t-mixture models\nwithout requiring stringent modeling constraints, making it\nsuitable for high-dimensional settings where the number of\nparameters exceeds the sample size. Reference: Kasa, S. R.,\nYijie, H., Kasa, S. K., & Rajan, V. (2024). Mixture-Models: a\none-stop Python Library for Model-based Clustering using\nvarious Mixture Models. arXiv preprint arXiv:2402.10229.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "SystemRequirements": "Python (>= 3.6), Python package 'mixture-models'\n(available on PyPI)", "URL": "https://github.com/kasakh/mixturemodelsr,\nhttps://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": { "Date": "2026-05-31 10:33:10 UTC", "User": "root" }, "Author": "Siva Rajesh Kasa [aut, cre, cph]", "Maintainer": "Siva 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