Package: CMF
Type: Package
Title: Collective Matrix Factorization
Version: 1.0.3
Date: 2022-08-09
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) <arXiv:1312.5921>.
	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) <https://research.cs.aalto.fi/pml/online-papers/klami13a.pdf>
	and	Virtanen et al. (2012) <http://proceedings.mlr.press/v22/virtanen12.html>
	for details on Bayesian CCA and GFA, respectively.
License: GPL (>= 2)
Imports: stats
LinkingTo: cpp11
Encoding: UTF-8
NeedsCompilation: yes
RoxygenNote: 7.2.1
BugReports: https://github.com/cyianor/CMF/issues
Packaged: 2022-08-09 19:11:09 UTC; hefelix
Author: Arto Klami [aut],
  Lauri Väre [aut],
  Felix Held [ctb, cre]
Maintainer: Felix Held <felix.held@gmail.com>
Repository: CRAN
Date/Publication: 2022-08-09 21:30:05 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-26 14:51:44 UTC; unix
Archs: CMF.so.dSYM
