Man pages for SDMtune
Species Distribution Model Selection

addSamplesToBgAdd Samples to Background
aiccAICc
ANN-classArtificial Neural Network
aucAUC
BRT-classBoosted Regression Tree
checkMaxentInstallationCheck Maxent Installation
confMatrixConfusion Matrix
corVarPrint Correlated Variables
doJkJackknife Test
getTunableArgsGet Tunable Arguments
gridSearchGrid Search
Maxent-classMaxent
maxentThMaxEnt Thresholds
maxentVarImpMaxent Variable Importance
Maxnet-classMaxnet
mergeSWDMerge SWD Objects
modelReportModel Report
optimizeModelOptimize Model
plotCorPlot Correlation
plotJkPlot Jackknife Test
plot-methodsPlot SDMtune object
plotPAPlot Presence Absence Map
plotPredPlot Prediction
plotResponsePlot Response Curve
plotROCPlot ROC curve
plotVarImpPlot Variable Importance
predict-ANN-methodPredict ANN
predict-BRT-methodPredict BRT
predict-Maxent-methodPredict Maxent
predict-RF-methodPredict RF
predict-SDMmodelCV-methodPredict for Cross Validation
predict-SDMmodel-methodPredict
prepareSWDPrepare an SWD object
randomFoldsCreate Random Folds
randomSearchRandom Search
reduceVarReduce Variables
RF-classRandom Forest
SDMmodel2MaxEntSDMmodel2MaxEnt
SDMmodel-classSDMmodel
SDMmodelCV-classSDMmodelCV
SDMtune-classSDMtune class
SDMtune-packageSDMtune: Species Distribution Model Selection
swd2csvSWD to csv
SWD-classSample With Data
thinDataThin Data
thresholdsThresholds
trainTrain
trainValTestTrain, Validation and Test datasets
tssTrue Skill Statistics
varImpVariable Importance
varSelVariable Selection
virtualSpVirtual Species
SDMtune documentation built on July 17, 2021, 9:06 a.m.