Package: poismf
Type: Package
Title: Factorization of Sparse Counts Matrices Through Poisson
        Likelihood
Version: 0.2.6
Date: 2020-11-13
Author: David Cortes [aut, cre, cph], Jean-Sebastien Roy [cph], Stephen Nash [cph]
Maintainer: David Cortes <david.cortes.rivera@gmail.com>
URL: https://github.com/david-cortes/poismf
BugReports: https://github.com/david-cortes/poismf/issues
Description: Creates a low-rank factorization of a sparse counts matrix by maximizing Poisson likelihood with l1/l2 regularization
	with all non-negative latent factors (e.g. for recommender systems or topic modeling) (Cortes, (2018) <arXiv:1811.01908>).
	Similar to hierarchical Poisson factorization, but follows an optimization-based approach with regularization instead of a
	hierarchical structure, and is fit through gradient-based methods instead of variational inference.
License: BSD_2_clause + file LICENSE
Imports: Matrix, methods
Enhances: SparseM
RoxygenNote: 7.1.1
NeedsCompilation: yes
Packaged: 2020-11-13 14:01:17 UTC; david
Repository: CRAN
Date/Publication: 2020-11-13 14:50:02 UTC
