{ "_id": "6abcc8e199f7aa8c6aa4bb79", "Package": "EMMIXmfa", "Type": "Package", "Title": "Mixture Models with Component-Wise Factor Analyzers", "Version": "2.0.71", "Date": "2018-12-14", "URL": "https://github.com/suren-rathnayake/EMMIXmfa", "Author": "Suren Rathnayake, Geoff McLachlan, David Peel, Jangsun Baek", "Maintainer": "Suren Rathnayake ", "Description": "We provide functions to fit finite mixtures of\nmultivariate normal or t-distributions to data with various\nfactor analytic structures adopted for the covariance/scale\nmatrices. The factor analytic structures available include\nmixtures of factor analyzers and mixtures of common factor\nanalyzers. The latter approach is so termed because the matrix\nof factor loadings is common to components before the\ncomponent-specific rotation of the component factors to make\nthem white noise. Note that the component-factor loadings are\nnot common after this rotation. Maximum likelihood estimators\nof model parameters are obtained via the\nExpectation-Maximization algorithm. See descriptions of the\nalgorithms used in McLachlan GJ, Peel D (2000)\n McLachlan GJ, Peel D (2000)\n McLachlan GJ, Peel D, Bean RW (2003)\n McLachlan GJ, Bean RW,\nBen-Tovim Jones L (2007) Baek\nJ, McLachlan GJ, Flack LK (2010) \nBaek J, McLachlan GJ (2011) \nMcLachlan GJ, Baek J, Rathnayake SI (2011)\n.", "License": "GPL (>= 2)", "Repository": "https://suren-rathnayake.r-universe.dev", "Date/Publication": "2019-02-23 22:31:33 UTC", "RemoteUrl": "https://github.com/suren-rathnayake/emmixmfa", "RemoteRef": "HEAD", "RemoteSha": "413ff590398c58e3c7596205b102cceaa827643b", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-09-30 07:53:43 UTC", "User": "root" }, "_user": "suren-rathnayake", "_type": "src", "_file": "EMMIXmfa_2.0.71.tar.gz", "_fileid": "https://r2.ropensci.org/ac002a14039f6f194b880aeb27165fe76af603055bbb2f4af0337b1f2a8bb694", "_filesize": 246279, "_sha256": "ac002a14039f6f194b880aeb27165fe76af603055bbb2f4af0337b1f2a8bb694", "_expires": "2027-01-08T08:31:27.000Z", 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