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  "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.",
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