Skip to content

randomMachines

0.1.1

An Ensemble Modeling using Random Machines

0packages depend
2.4Kdownloads / year
test coverage
13/13checks pass

Overview

About
Maintained by Mateus MaiaFirst published 2023-12-142 releasesCRAN page ↗

A novel ensemble method employing Support Vector Machines (SVMs) as base learners. This powerful ensemble model is designed for both classification (Ara A., et. al, 2021) doi:10.6339/21-JDS1014, and regression (Ara A., et. al, 2021) doi:10.1016/j.eswa.2022.117107 problems, offering versatility and robust performance across different datasets and compared with other consolidated methods as Random Forests (Maia M, et. al, 2021) doi:10.6339/21-JDS1025.

Install

Health

CRAN checks
13OK
Slowest check: 1.2 min · r-devel-windows-x86_64
Code health
None
Tests · ratio 0.00
not tracked
Coverage
100%
Documentation · exports
3
Dependencies · direct
Check history
  • OK2026-08-05
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • NOTE2026-08-01
    12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • OK2026-05-12
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • WARNING2026-05-11
    12 OK · 0 NOTE · 1 WARNING · 0 ERROR · 0 FAILURE
  • OK2026-03-10
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation
READMEYes · 41 wordsVignettesNopkgdown siteNoNEWSNoCode of conductNoContributing guideNo
Examples that run
100%
Documented parameters
100%
Return-value docs
100%
References docs
50%

Downloads

2.4K
CRAN downloads in the past year
Rank #21,944 · ~7/day · ~204/mo
Daily download trend is not available in this view yet.
13630 days
49890 days
2.4K1 year
Compare downloads with other packages →
Also on105 r2u20 autocran

Dependencies

Declared dependencies
1 external dependency (excludes base and recommended)
Depends (1)
R >= 2.10
Imports (3)
kernlabmethodsstats
LinkingTo (0)
none
Suggests (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Code & Tests

Datasets

People & History

People (3)
Maintainer (1)
Author, Maintainer
Authors (1)
Author, Maintainer
Other (2)
Package Timeline

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

  • R
    R 4.6.0 released · 2026-04-24
  • 0.1.1Latest
    2025-07-23 · current release · diff ↗
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • 0.1.0
    2023-12-14
  • R
    R 4.3.0 released · 2023-04-21

Package metadata

First published
2023-12-14
Total releases
2 / 3 yrs
License
MIT + file LICENSE OSI
Minimum R
≥ 2.10
Bundled data
178 KB / 3 files
Download size
196 KB
Installed size
not tracked yet
With dependencies
not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("randomMachines")
Maia, M., Ara, A., & Ribeiro, G. (2025). randomMachines: An Ensemble Modeling using Random Machines (Version 0.1.1) [Computer software]. https://doi.org/10.32614/CRAN.package.randomMachines

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

Cite the R Observatory

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

APA

Balamuta, J. J. (2026). R Observatory: Metrics for randomMachines version 0.1.1 [Data set]. HJJB, LLC. Data release v2026-08-17. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-17, which the citation names so these numbers can be found later. More on citing and the projects behind them.

Report a problem with this page →

Privacy choices

These apply to this browser and are stored on this device only. Nothing about your choice is sent to us.

Read the privacy policy