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optRF

1.2.1Package record

Optimising Random Forest Stability by Determining the Optimal Number of Trees

0packages depend
6.1Kdownloads / year
–test coverage
13/13flavors OK

Overview

About

Calculating the stability of random forest with certain numbers of trees. The non-linear relationship between stability and numbers of trees is described using a logistic regression model and used to estimate the optimal number of trees.

Changes in 1.2.1

Improvements

  • Improved output of estimate_numtrees and estimate_stability functions

Bug Fixes

  • Fixed a bug with the plot functions for the opt_importance_object and opt_prediction_object objects.

From NEWS.md in the release

Install

Health

CRAN checks
13OK
Slowest check: 2.2 min · r-oldrel-windows-x86_64
Status changes
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK14 OK2026-03-10
Advisories

none known

Documentation

In release 1.2.1
READMEYes · 246 wordsVignettesYes · 3 · dynamicNEWSYes · 100% structured · mentions 1.2.1Citation fileYes · cites a publicationWebsitenot declared in DESCRIPTION
Help pages
7
Examples that run
14%1 run in a plain check · 6 never run
Exported functions with examples
100%
Documented parameters
100%
Return-value docs
100%
Help pages citing references
14%

Downloads

6.1K
CRAN downloads in the past year
Rank #10,131 · ~17/day · ~509/mo
Daily download trend is not available in this view yet.
33030 days
98890 days
6.1K1 year
Compare downloads with other packages →
Also on73 r2u6 autocran

Repository

2Stars
3Forks
0Open issues
0Open PRs
0Releases
118Commits
3Commit identities
118 commits · Last activity 2026-01-19 · 0% stars, 30d · Default branch at 2.0.0, ahead of CRAN 1.2.1 (checked 2026-10-18)
Responsiveness
How these are sampled
PRs merged (all time)
78%
Median days to close, 50 most recently updated closed issues
not measured
Median days to close, 50 most recently updated closed PRs
0

Stars over time

2025-04-07 · 22026-07-07 · 2
PracticesBeta

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

Checks run against github.com/tmlange/optrf on 2026-10-18.

Reproducibility and dev environment (1)
data-raw/
How this is detected·Detection ruleset v3 (2026-09-26)

Dependencies

Declared dependencies
8 external dependencies (excludes base and recommended)

Installing optRF adds 6 packages (5 with compiled code, 4.2 MB of source tarballs). Heaviest: Rcpp 1.8 MB, RcppEigen 1.7 MB, lpSolve 457 KB.

Depends (1)
R >= 3.6.0
Imports (6)
minpack.lmrangerirrgraphicsmethodsstats
LinkingTo (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Datasets

People & History

People (2)
Maintainer since 1.0.1 (2024), Author
Contributor
Listed in earlier versions (1)
Lange
no longer listed · 1.1.0 to 1.2.1
Releases (4)

Pick two releases to compare their code metrics.

  • R
    R 4.6.0 released · 2026-04-24
  • 1.2.1Latest
    record
    2025-05-08 · current release · diff ↗
  • R
    R 4.5.0 released · 2025-04-11
  • 1.2.0
    record
    2025-04-01 · diff ↗ ·
    added 1 export
    measure_stability
  • 1.1.0
    record
    2025-02-03 · diff ↗
  • 1.0.1
    record
    2024-09-23
  • R
    R 4.4.0 released · 2024-04-24

Package metadata

First published
2024-09-23
Total releases
4 / 2 yrs
License
GPL (>= 2) OSI
Minimum R
≥ 3.6.0
Bundled data
158 KB / 1 file
Download size
1.1 MB

Cite

Cite this package

Run in R for the authors' entry:

citation("optRF")

The package provides a citation file, so citation() returns the authors' own entry.

It points to 10.1186/s12859-025-06097-1.

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 optRF version 1.2.1 [Data set]. HJJB, LLC. Data release v2026-10-20. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-20, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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