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]
First Published: 2019-04-12
Latest Update: 2022-09-11
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 26 Mar. 2025.
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
Downloads during the last 30 days
02/2402/2502/2602/2702/2803/0103/0203/0303/0403/0503/0603/0703/0803/0903/1003/1103/1203/1303/1403/1503/1603/1703/1803/1903/2003/2103/2203/2303/2403/25Downloads for rsparse140160180200220240260280300320340360380400420440TrendBars

Today's Hot Picks in Authors and Packages

sAIC  
Akaike Information Criterion for Sparse Estimation
Computes the Akaike information criterion for the generalized linear models (logistic regression, Po ...
Download / Learn more Package Citations See dependency  
provenance  
Statistical Toolbox for Sedimentary Provenance Analysis
Bundles a number of established statistical methods to facilitate the visual interpretation of large ...
Download / Learn more Package Citations See dependency  
MGL  
Module Graphical Lasso
An aggressive dimensionality reduction and network estimationtechnique for a high-dimensiona ...
Download / Learn more Package Citations See dependency  
fPortfolio  
Rmetrics - Portfolio Selection and Optimization
A collection of functions to optimize portfolios and to analyze them from different points of view. ...
Download / Learn more Package Citations See dependency  
DySS  
Dynamic Screening Systems
In practice, we will encounter problems where the longitudinal performance of processes needs to be ...
Download / Learn more Package Citations See dependency  
Maintainer: Lu You (view profile)
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  

23,842

R Packages

207,311

Dependencies

64,420

Author Associations

23,781

Publication Badges

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