Package: CMF Type: Package Title: Collective Matrix Factorization Version: 1.0.3.99 Authors@R: c( person(given = "Arto", family = "Klami", email = "arto.klami@cs.helsinki.fi", role = "aut"), person(given = "Lauri", family = "Väre", role = "aut"), person(given = "Felix", family = "Held", email = "felix.held@gmail.com", role = c("ctb", "cre"))) Description: Collective matrix factorization (CMF) finds joint low-rank representations for a collection of matrices with shared row or column entities. This code learns a variational Bayesian approximation for CMF, supporting multiple likelihood potentials and missing data, while identifying both factors shared by multiple matrices and factors private for each matrix. For further details on the method see Klami et al. (2014) . The package can also be used to learn Bayesian canonical correlation analysis (CCA) and group factor analysis (GFA) models, both of which are special cases of CMF. This is likely to be useful for people looking for CCA and GFA solutions supporting missing data and non-Gaussian likelihoods. See Klami et al. (2013) and Virtanen et al. (2012) for details on Bayesian CCA and GFA, respectively. License: GPL (>= 2) Imports: stats LinkingTo: cpp11 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: 2026-07-04 05:45:38 UTC; root Author: Arto Klami [aut], Lauri Väre [aut], Felix Held [ctb, cre] Maintainer: Felix Held