https://github.com/jeffreyevans/rfutilities

R package for random forests model selection, inference, evaluation and validation
https://github.com/jeffreyevans/rfutilities

classification cran nonparametric-statistics r r-package random-forest regression statistics

Last synced: over 1 year ago
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R package for random forests model selection, inference, evaluation and validation

• Host: GitHub
• URL: https://github.com/jeffreyevans/rfutilities
• Owner: jeffreyevans
• License: gpl-3.0
• Created: 2014-11-20T23:18:33.000Z (over 11 years ago)
• Default Branch: master
• Last Pushed: 2024-03-28T08:09:43.000Z (about 2 years ago)
• Last Synced: 2025-02-27T03:23:14.839Z (over 1 year ago)
• Topics: classification, cran, nonparametric-statistics, r, r-package, random-forest, regression, statistics
• Language: R
• Homepage:
• Size: 306 KB
• Stars: 24
• Watchers: 3
• Forks: 13
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# rfUtilities (CRAN 2.1-5, development 2.2-0)

[![CRAN

status](http://www.r-pkg.org/badges/version/rfUtilities)](https://cran.r-project.org/package=rfUtilities)

[![CRAN RStudio mirror

downloads](http://cranlogs.r-pkg.org/badges/grand-total/rfUtilities)](https://cran.r-project.org/package=rfUtilities)

*R package for random forests model selection, class balance and validation*

Random Forests Model Selection, inference, fit and performance evaluation

rfUtilities 2.2-0 (GitHub development release) 

- added ranger random forests implementation support

# Available functions in rfUtilities 2.2-0 are:

| Code                         | Description                                                                             |

|:-----------------------------|----------------------------------------------------------------------------------------|

| `accuracy`                     | Calculates suite of accuracy statistics for classification or regression models (called by rf.crossValidation)

| `bivariate.partialDependence`  | Bivariate partial-dependency plot

| `collinear`                    | Evaluation of pair-wise linear or nonlinear correlations in data

| `ensembleTest`                 | (experimental) test for degree of correlation across the ensemble, over correlation can indicate overfit

| `logLoss`                      | Calculates Logarithmic or Likelihood loss function

| `multi.collinear`              | Multi-collinearity test with matrix permutation.

| `occurrence.threshold`         | A statistical sensitivity test for occurrence probability thresholds

| `probability.calibration`      | Isotonic probability calibration

| `ranger.proximity`             | Derives a proximity matrix for a ranger object

| `rf.class.sensitivity`         | Random Forests class-level sensitivity analysis

| `rf.classBalance`              | Random Forests Class Balance (Zero Inflation Correction) Model with covariance convergence

| `rf.combine`                   | Combine Random Forests Ensembles 

| `rf.crossValidation`           | Random Forests classification or regression cross-validation, added simplified arguments and ranger support  

| `rf.effectSize`                | Random Forests class-level parameter effect size 

| `rf.imp.freq`                  | Random Forests variable selection frequency

| `rf.modelSel`                  | Random Forests Model Selection, simplified arguments and added ranger support

| `rf.partial.ci`                | Random Forests regression partial dependency plot with confidence intervals

| `rf.partial.prob`              | Random Forest probability scaled partial dependency plots

| `rf.regression.fit`            | Evaluates fit and overfit of random forests regression models

| `rf.significance`              | Significance test for classification or regression random forests models, simplified arguments and added ranger support 

| `rf.unsupervised`              | Unsupervised Random Forests with cluster support

| `spatial.uncertainty`          | (experimental) creates spatial estimate of uncertainty using an Infinitesimal Jackknife to calculate standard errors  

 

**Bugs**: Users are encouraged to report bugs here. Go to [issues](https://github.com/jeffreyevans/rfUtilities/issues) in the menu above, and press new issue to start a new bug report, documentation correction or feature request. You can direct questions to .

**To install `rfUtilities` in R use install.packages() to download current stable release from CRAN** 

**or, for the development version, run the following (requires the remotes package):**

`remotes::install_github("jeffreyevans/rfUtilities")`

**Tutorial**: See (http://evansmurphy.wixsite.com/evansspatial/random-forest-sdm).