Species Distribution Model Selection

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Documentation for package ‘SDMtune’ version 1.3.1

Help Pages

addSamplesToBg Add Samples to Background
aicc AICc
ANN Artificial Neural Network
ANN-class Artificial Neural Network
auc AUC
BRT Boosted Regression Tree
BRT-class Boosted Regression Tree
checkMaxentInstallation Check Maxent Installation
combineCV Combine Cross Validation models
confMatrix Confusion Matrix
corVar Print Correlated Variables
doJk Jackknife Test
getTunableArgs Get Tunable Arguments
gridSearch Grid Search
Maxent Maxent
Maxent-class Maxent
maxentTh MaxEnt Thresholds
maxentVarImp Maxent Variable Importance
Maxnet Maxnet
Maxnet-class Maxnet
mergeSWD Merge SWD Objects
modelReport Model Report
optimizeModel Optimize Model
plot-method SDMtune class
plotCor Plot Correlation
plotJk Plot Jackknife Test
plotPA Plot Presence Absence Map
plotPred Plot Prediction
plotResponse Plot Response Curve
plotROC Plot ROC curve
plotVarImp Plot Variable Importance
predict-method Predict ANN
predict-method Predict BRT
predict-method Predict Maxent
predict-method Predict Maxnet
predict-method Predict RF
predict-method Predict
predict-method Predict for Cross Validation
prepareSWD Prepare an SWD object
randomFolds Create Random Folds
randomSearch Random Search
reduceVar Reduce Variables
RF Random Forest
RF-class Random Forest
SDMmodel SDMmodel
SDMmodel-class SDMmodel
SDMmodel2MaxEnt SDMmodel2MaxEnt
SDMmodelCV SDMmodelCV
SDMmodelCV-class SDMmodelCV
SDMtune SDMtune class
SDMtune-class SDMtune class
show-method Artificial Neural Network
show-method Boosted Regression Tree
show-method Maxent
show-method Maxnet
show-method Random Forest
show-method SDMmodel
show-method SDMmodelCV
show-method SDMtune class
show-method Sample With Data
SWD Sample With Data
SWD-class Sample With Data
swd2csv SWD to csv
thinData Thin Data
thresholds Thresholds
train Train
trainValTest Train, Validation and Test datasets
tss True Skill Statistics
varImp Variable Importance
varSel Variable Selection
virtualSp Virtual Species