RoughSets

1.3-8

Data Analysis Using Rough Set and Fuzzy Rough Set Theories

0packages depend

6.4Kdownloads / year

–test coverage

13/13checks pass

Overview

About

Maintained by Christoph BergmeirFirst published 2014-02-047 releasesCRAN page ↗GitHub ↗

Implementations of algorithms for data analysis based on the rough set theory (RST) and the fuzzy rough set theory (FRST). We not only provide implementations for the basic concepts of RST and FRST but also popular algorithms that derive from those theories. The methods included in the package can be divided into several categories based on their functionality: discretization, feature selection, instance selection, rule induction and classification based on nearest neighbors. RST was introduced by Zdzisław Pawlak in 1982 as a sophisticated mathematical tool to model and process imprecise or incomplete information. By using the indiscernibility relation for objects/instances, RST does not require additional parameters to analyze the data. FRST is an extension of RST. The FRST combines concepts of vagueness and indiscernibility that are expressed with fuzzy sets (as proposed by Zadeh, in 1965) and RST.

Install

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

install.packages("RoughSets", 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("janusza/RoughSets")

Development version from janusza/RoughSets. 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-roughsets

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled.

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-RoughSets

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-RoughSets

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed).

Health

CRAN checks

13OK

Slowest check: 2.5 min · r-devel-linux-x86_64-fedora-clang

Code health

None

Tests · ratio 0.00

not tracked

Coverage

100%

Documentation · exports

Dependencies · direct

Check history

OK2026-04-22
14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
ERROR2026-04-18
13 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
OK2026-03-10
14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

READMENoVignettesNopkgdown siteNoNEWSNoCode of conductNoContributing guideNo

Examples that run: 100%
Documented parameters: 100%
Return-value docs: 100%
References docs: 62%

Downloads

6.4K

CRAN downloads in the past year

Rank #9,345 · ~18/day · ~534/mo

Releases R versions

Daily download trend is not available in this view yet.

30130 days

1.1K90 days

6.4K1 year

Compare downloads with other packages →

Also on429 r2u2.6K conda_forge87 c2d4u

Repository

GitHub ↗ janusza/roughsets

38Stars

13Forks

3Open issues

0Open PRs

0Releases

80Commits

4Contributors

80 commits · Last activity 2020-01-25 · 0% stars, 30d

Stars over time

2024-10-26 · 362026-07-07 · 38

Repository practices

Upstream repositoryBeta

1 development-tooling and community-health practice detected across 1 family in the upstream repository

Checks run against github.com/janusza/roughsets on 2026-07-30.

Git structural (1)

.gitattributes

How this is detected·Detection ruleset v1 (2026-07-18)

Dependencies

Declared dependencies

2 external dependencies (excludes base and recommended)

Depends (1)

Rcpp

Imports (0)

none

LinkingTo (1)

Rcpp

Suggests (1)

class

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

Code & Tests

People & History

People (8)

Maintainer (1)

Christoph Bergmeir

Contributor, Maintainer · added in 1.2-0

Authors (2)

Andrzej Janusz

Author

Lala Septem Riza

Author

Contributors (6)

Christoph Bergmeir

Contributor, Maintainer · added in 1.2-0

Jose Manuel Benitez

Contributor · added in 1.2-0

Chris Cornelis

Contributor

Francisco Herrera

Contributor

Sebastian Stawicki

Contributor · added in 1.2-0

Dominik Ślęzak

Contributor

Listed in earlier versions (2)

and Jose Manuel Benitez

no longer listed · 1.0-0 to 1.1-0

Dominik Slezak

no longer listed · 1.0-0 to 1.1-0

Package Timeline

7 releases. Pick two to compare their code metrics. R releases are shown for context.

R
R 4.6.0 released · 2026-04-24
R
R 4.5.0 released · 2025-04-11
R
R 4.4.0 released · 2024-04-24
1.3-8Latest
2024-01-23 · current release · diff ↗
R
R 4.3.0 released · 2023-04-21
R
R 4.2.0 released · 2022-04-22
R
R 4.1.0 released · 2021-05-18
R
R 4.0.0 released · 2020-04-24
1.3-7
2019-12-15 · diff ↗
R
R 3.6.0 released · 2019-04-26
R
R 3.5.0 released · 2018-04-23
R
R 3.4.0 released · 2017-04-21
R
R 3.3.0 released · 2016-05-03
1.3-0
2015-09-05 · diff ↗
R
R 3.2.0 released · 2015-04-16
1.2-1
2015-03-24 · diff ↗

Show 5 earlier events

1.2-0
2015-03-19 · diff ↗
1.1-0
2014-06-19 · diff ↗
R
R 3.1.0 released · 2014-04-10
1.0-0
2014-02-04
R
R 3.0.0 released · 2013-04-03

Package metadata

First published: 2014-02-04
Total releases: 7 / 12 yrs
License: GPL (>= 2) OSI
Bundled data: 42 KB / 1 file
Download size: 229 KB
Installed size: not tracked yet
With dependencies: not tracked yet

Appears in task views

Machine Learning & Statistical Learning

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