CRAN/E | rsparse

rsparse

Statistical Learning on Sparse Matrices

Installation

About

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(, doi:10.1145/2487575.2488200) 2) Factorization Machines via SGD, as per Rendle (2010, doi:10.1109/ICDM.2010.127) 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, doi:10.1109/ICDM.2008.22) 2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro (2005, doi:10.1145/1102351.1102441) 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.

github.com/rexyai/rsparse
Bug report File report

Key Metrics

Version 0.5.1
R ≥ 3.6.0
Published 2022-09-11 762 days ago
Needs compilation? yes
License GPL-2
License GPL-3
CRAN checks rsparse results

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Maintainer

Maintainer

Dmitriy Selivanov

Authors

Dmitriy Selivanov

aut / cre / cph

David Cortes

ctb

Drew Schmidt

ctb

(configure script for BLAS, LAPACK detection)

Wei-Chen Chen

ctb

(configure script and work on linking to float package)

Material

README
NEWS
Reference manual
Package source

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macOS

r-release

arm64

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arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

rsparse archive

Depends

R ≥ 3.6.0
methods
Matrix ≥ 1.3

Imports

MatrixExtra ≥ 0.1.7
Rcpp ≥ 0.11
data.table ≥1.10.0
float ≥ 0.2-2
RhpcBLASctl
lgr ≥ 0.2

Suggests

testthat
covr

LinkingTo

Rcpp
RcppArmadillo ≥ 0.9.100.5.0

Reverse Imports

LSX
PsychWordVec
text2vec