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rsparse  

Statistical Learning on Sparse Matrices
View on CRAN: Click here


Download and install rsparse package within the R console
Install from CRAN:
install.packages("rsparse")

Install from Github:
library("remotes")
install_github("cran/rsparse")

Install by package version:
library("remotes")
install_version("rsparse", "0.5.3")



Attach the package and use:
library("rsparse")
Maintained by
Dmitriy Selivanov
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-04-12
Latest Update: 2025-02-17
Description:
Implements many algorithms for statistical learning on sparse matrices - matrix factorizations, matrix completion, elastic net regressions, factorization machines. Also 'rsparse' enhances 'Matrix' package by providing methods for multithreaded matrix products and native slicing of the sparse matrices in Compressed Sparse Row (CSR) format. List of the algorithms for regression problems: 1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL) Stochastic Gradient Descent (SGD), as per McMahan et al(, ) 2) Factorization Machines via SGD, as per Rendle (2010, ) List of algorithms for matrix factorization and matrix completion: 1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least Squares (ALS) - paper by Hu, Koren, Volinsky (2008, ) 2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro (2005, ) 3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder et al. (2014, ) 4) Linear-Flow matrix factorization, from 'Practical linear models for large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al (2016, ISBN:978-1-57735-770-4) 5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington, Socher, Manning (2014, ) Package is reasonably fast and memory efficient - it allows to work with large datasets - millions of rows and millions of columns. This is particularly useful for practitioners working on recommender systems.
How to cite:
Dmitriy Selivanov (2019). rsparse: Statistical Learning on Sparse Matrices. R package version 0.5.3, https://cran.r-project.org/web/packages/rsparse. Accessed 25 Jun. 2026.
Previous versions and publish date:
0.3.3.1 (2019-04-14 22:13), 0.3.3.2 (2019-07-18 15:30), 0.3.3.3 (2019-08-04 12:00), 0.3.3.4 (2019-11-14 13:30), 0.3.3 (2019-04-12 10:42), 0.4.0 (2020-04-01 19:50), 0.5.0 (2021-11-30 08:50), 0.5.1 (2022-09-12 00:20), 0.5.2 (2024-06-28 11:30)
Other packages that cited rsparse R package
View rsparse citation profile
Other R packages that rsparse depends, imports, suggests or enhances
Complete documentation for rsparse
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