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jeffreyevans / rfUtilities

R package for random forests model selection, inference, evaluation and validation
GNU General Public License v3.0
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classification cran nonparametric-statistics r r-package random-forest regression statistics

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

CRAN status CRAN RStudio mirror downloads

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)

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 in the menu above, and press new issue to start a new bug report, documentation correction or feature request. You can direct questions to jeffrey_evans@tnc.org.

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).