{ "_id": "6a212855cd65a98ecbd21bfa", "Package": "CMF", "Type": "Package", "Title": "Collective Matrix Factorization", "Version": "1.0.3.99", "Authors@R": "c(\nperson(given = \"Arto\", family = \"Klami\", email = \"arto.klami@cs.helsinki.fi\",\nrole = \"aut\"),\nperson(given = \"Lauri\", family = \"Väre\", role = \"aut\"),\nperson(given = \"Felix\", family = \"Held\", email = \"felix.held@gmail.com\",\nrole = c(\"ctb\", \"cre\")))", "Description": "Collective matrix factorization (CMF) finds joint low-rank\nrepresentations for a collection of matrices with shared row or\ncolumn entities. This code learns a variational Bayesian\napproximation for CMF, supporting multiple likelihood\npotentials and missing data, while identifying both factors\nshared by multiple matrices and factors private for each\nmatrix. For further details on the method see Klami et al.\n(2014) . The package can also be used to learn\nBayesian canonical correlation analysis (CCA) and group factor\nanalysis (GFA) models, both of which are special cases of CMF.\nThis is likely to be useful for people looking for CCA and GFA\nsolutions supporting missing data and non-Gaussian likelihoods.\nSee Klami et al. (2013)\n\nand Virtanen et al. (2012)\n for details\non Bayesian CCA and GFA, respectively.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "Language": "en-US", "NeedsCompilation": "yes", "RoxygenNote": "7.2.3", "Roxygen": "list(markdown = TRUE)", "URL": "https://github.com/cyianor/CMF", "BugReports": "https://github.com/cyianor/CMF/issues", "Repository": "https://cyianor.r-universe.dev", "Date/Publication": "2023-04-26 09:50:13 UTC", "RemoteUrl": "https://github.com/cyianor/cmf", "RemoteRef": "HEAD", "RemoteSha": "93d52dbd4a6d7edc5b7c43be823d82546894c49f", "Packaged": { "Date": "2026-06-04 07:17:54 UTC", "User": "root" }, "Author": "Arto Klami [aut],\nLauri Väre [aut],\nFelix Held [ctb, cre]", "Maintainer": "Felix Held ", "MD5sum": "2a59fac1e038e46523df38302517ecdf", "_user": "cyianor", "_type": "src", "_file": "CMF_1.0.3.99.tar.gz", "_fileid": "1794f4460d0046b35b65a2283933fe6cbffe2b601a6114f69155f9ab0579aa90", "_filesize": 126898, "_sha256": "1794f4460d0046b35b65a2283933fe6cbffe2b601a6114f69155f9ab0579aa90", 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