Other packages > Find by keyword >

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 08 Oct. 2026.
Previous versions and publish date:
(2026-07-09 06:53), 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
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

dineR  
Differential Network Estimation in R
An efficient and convenient set of functions to perform differential network estimation through the ...
Download / Learn more Package Citations See dependency  
nextGenShinyApps  
Craft Exceptional 'R Shiny' Applications and Dashboards with Novel Responsive Tools
Nove responsive tools for designing and developing 'Shiny' dashboards and applications. The scripts ...
Download / Learn more Package Citations See dependency  
climwin  
Climate Window Analysis
Contains functions to detect and visualise periods of climate sensitivity (climate windows) for a g ...
Download / Learn more Package Citations See dependency  
colorfindr  
Extract Colors from Windows BMP, JPEG, PNG, TIFF, and SVG Format Images
Extracts colors from various image types, returns customized reports and plots treemaps and 3D scat ...
Download / Learn more Package Citations See dependency  
metafor  
Meta-Analysis Package for R
A comprehensive collection of functions for conducting meta-analyses in R. The package includes func ...
Download / Learn more Package Citations See dependency  
abc.data  
Data Only: Tools for Approximate Bayesian Computation (ABC)
Contains data which are used by functions of the 'abc' package. ...
Download / Learn more Package Citations See dependency  

28,905

R Packages

247,686

Dependencies

76,495

Author Associations

28,906

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA