R Observatory: Metrics for rsparse version 0.5.3

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

rsparse

0.5.3Package record

Statistical Learning on Sparse Matrices

1packages dependsuggested by 2

190.2Kdownloads / year

72.1%test coverage

11/13flavors OK

Overview

About

Maintained by Dmitriy SelivanovCRAN page ↗GitHub ↗Issues ↗

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 <sparse, dense> 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, doi:10.48550/arXiv.1410.2596) 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, https://aclanthology.org/D14-1162/) 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.

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("rsparse", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("dselivanov/rsparse")

Development version from dselivanov/rsparse. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-rsparse

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled. We list this channel from recorded r2u download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-rsparse

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-rsparse

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed). We list this channel from recorded autoCRAN download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

Health

CRAN checks

2NOTE11OK

Check messages

NOTEdependencies in R coder-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc

Namespace in Imports field not imported from:
  ‘MatrixExtra’
All declared Imports should be used.

CRAN check page ↗

Slowest check: 7.1 min · r-oldrel-windows-x86_64

Status changes

NOTE11 OK, 2 NOTE2026-10-17
ERROR10 OK, 2 NOTE, 1 ERROR2026-10-15
NOTE10 OK, 4 NOTE2026-04-22
ERROR9 OK, 4 NOTE, 1 ERROR2026-04-18
NOTE10 OK, 4 NOTE2026-03-10

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 0.5.3

READMEYes · 828 wordsVignettesNoNEWSYes · 100% structured · does not mention 0.5.3Citation fileNoWebsitenot declared in DESCRIPTION

Help pages: 13 (2 internal)
Examples that run: 100%8 run in a plain check
Exported functions with examples: 80%
Documented parameters: 100%
Return-value docs: 10%
Help pages citing references: 31%

How these are measured

Downloads

190.2K

CRAN downloads in the past year

Rank #1,686 · ~521/day · ~15.9K/mo

Releases R versions

Daily download trend is not available in this view yet.

4.3K30 days

12.9K90 days

190.2K1 year

Compare downloads with other packages →

Also on758 r2u4 autocran4.2K conda_forge136 c2d4u

How downloads are counted

Repository

dselivanov/rsparse ↗Account: dselivanov

181Stars

31Forks

7Open issues

3Open PRs

0Releases

350Commits

6Commit identities

collaborative-filteringrecommender-systemrmatrix-factorizationmatrix-completionfactorization-machinessvdsparse-matrices

350 commits · Last activity 2025-02-17 · +0.6% stars, 30d

Responsiveness

How these are sampled

PRs merged (all time): 88%
Median days to close, 50 most recently updated closed issues: 13
Median days to close, 50 most recently updated closed PRs: 2

Stars over time

2025-09-22 · 1782026-10-19 · 181

How these are measured

PracticesBeta

6 development-tooling and community-health practices detected across 5 families in the upstream repository

Checks run against github.com/dselivanov/rsparse on 2026-10-22.

Continuous integration (2)

GitHub ActionsR CMD check workflow

The R CMD check workflow runs on Linux, and on R-devel

Coverage (1)

Coverage reporting

Reproducibility and dev environment (1)

Makefile

CRAN release process (1)

cran-comments.md

Show all practicesHide 1 practice

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v4 (2026-10-20)

Dependencies

Declared dependencies

10 external dependencies (excludes base and recommended)

Installing rsparse adds 8 packages (6 with compiled code, 11 MB of source tarballs). Heaviest: data.table 5.7 MB, Rcpp 1.8 MB, float 1.3 MB.

Depends (3)

R >= 3.6.0methodsMatrix

Imports (6)

MatrixExtra Rcpp data.table (>= 1.10.0)float RhpcBLASctl lgr

LinkingTo (2)

Rcpp RcppArmadillo

Suggests (2)

testthat covr

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

1direct

21indirect

Imports 1

Suggested by 2

Depends, Imports or LinkingTo, most downloaded first

text2vec

Suggests, most downloaded first

LSX PsychWordVec

How these are counted

Code & Tests

Datasets

People & History

People (4)

Dmitriy Selivanov

Wei-Chen Chen

Contributor

configure script and work on linking to float package

David Cortes

Contributor · added in 0.5.0

Drew Schmidt

Contributor

configure script for BLAS, LAPACK detection

Releases (10)

Pick two releases to compare their code metrics.

R
R 4.6.0 released · 2026-04-24
R
R 4.5.0 released · 2025-04-11
0.5.3Latest
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2025-02-17 · current release · diff ↗
0.5.2
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2024-06-28 · diff ↗
R
R 4.4.0 released · 2024-04-24
R
R 4.3.0 released · 2023-04-21
0.5.1
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2022-09-12 · diff ↗
R
R 4.2.0 released · 2022-04-22
0.5.0
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2021-11-30 · diff ↗
R
R 4.1.0 released · 2021-05-18
R
R 4.0.0 released · 2020-04-24
0.4.0
record
2020-04-01 · diff ↗ ·
added 1 export
ScaleNormalize
0.3.3.4
record
2019-11-14 · diff ↗
0.3.3.3
record
2019-08-04 · diff ↗
0.3.3.2
record
2019-07-18 · diff ↗
R
R 3.6.0 released · 2019-04-26

Show 3 earlier eventsHide 3 earlier events

0.3.3.1
record
2019-04-14 · diff ↗
0.3.3
record
2019-04-12
R
R 3.5.0 released · 2018-04-23

Package metadata

First published: 2019-04-12
Total releases: 10 / 7 yrs
License: GPL (>= 2) OSI
Minimum R: ≥ 3.6.0
Bundled data: 98 KB / 1 file
Download size: 210 KB

Appears in task views

Missing Data

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("rsparse")

Format

Selivanov, D., Chen, W., Cortes, D., & Schmidt, D. (2025). rsparse: Statistical Learning on Sparse Matrices (Version 0.5.3) [Computer software]. https://doi.org/10.32614/CRAN.package.rsparse

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for rsparse version 0.5.3 [Data set]. HJJB, LLC. Data release v2026-10-23. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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