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poismf
View on CRAN: Click
here
Download and install poismf package within the R console
Install from CRAN:
install.packages("poismf")
Install from Github:
library("remotes")
install_github("cran/poismf") Install by package version:
library("remotes")
install_version("poismf", "0.4.0-4") Attach the package and use:
library("poismf")
Maintained by
David Cortes
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-08-01
Latest Update: 2023-03-26
Description:
Creates a non-negative low-rank approximate factorization of a sparse counts matrix by maximizing Poisson
likelihood with L1/L2 regularization (e.g. for implicit-feedback recommender systems or bag-of-words-based topic modeling)
(Cortes, (2018) ), which usually leads to very sparse user and item factors (over 90% zero-valued).
Similar to hierarchical Poisson factorization (HPF), but follows an optimization-based approach with regularization
instead of a hierarchical prior, and is fit through gradient-based methods instead of variational inference.
How to cite:
David Cortes (2019). poismf: Factorization of Sparse Counts Matrices Through Poisson Likelihood. R package version 0.4.0-4, https://cran.r-project.org/web/packages/poismf. Accessed 08 Jun. 2026.
Previous versions and publish date:
0.1.1 (2019-08-01 11:00), 0.1.2 (2019-08-29 21:10), 0.1.3 (2019-09-05 04:20), 0.2.0 (2020-05-27 00:10), 0.2.3 (2020-08-10 18:40), 0.2.4 (2020-08-26 11:10), 0.2.5 (2020-09-12 13:40), 0.2.6 (2020-11-13 15:50), 0.2.7 (2021-01-08 21:20), 0.3.0-1 (2021-05-08 06:10), 0.3.0 (2021-04-06 09:10), 0.3.1-1 (2021-08-21 06:30), 0.3.1-2 (2021-09-02 07:40), 0.3.1-3 (2021-09-26 06:10), 0.3.1 (2021-07-13 06:50), 0.4.0-1 (2022-08-15 23:40), 0.4.0-2 (2022-10-25 19:12), 0.4.0-3 (2023-01-13 20:40), 0.4.0 (2022-02-25 09:00)
Other packages that cited poismf R package
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Other R packages that poismf depends,
imports, suggests or enhances
Complete documentation for poismf
Functions, R codes and Examples using
the poismf R package
Some associated functions: factors . factors.single . get.factor.matrices . get.model.mappings . poismf . poismf_unsafe . predict.poismf . print.poismf . summary.poismf . topN.new . topN .
Some associated R codes: poismf.R . Full poismf package functions and examples
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