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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]
[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,
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 05 Aug. 2026.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for rsparse
Functions, R codes and Examples using
the rsparse R package
Some associated functions: FTRL . FactorizationMachine . GloVe . LinearFlow . MatrixFactorizationRecommender . PureSVD . ScaleNormalize . WRMF . detect_number_omp_threads . metrics . movielens100k . soft_impute . train_test_split .
Some associated R codes: MatrixFactorizationRecommender.R . RcppExports.R . SoftALS.R . data.R . kmeans.R . metrics.R . model_FTRL.R . model_FactorizationMachine.R . model_GloVe.R . model_LinearFlow.R . model_PureSVD.R . model_RankMF.R . model_ScaleNormalize.R . model_WRMF.R . splr.R . utils.R . utils_SoftALS.R . zzz.R . Full rsparse package functions and examples
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