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

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 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).