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SDMtune (version 1.1.0)

Species Distribution Model Selection

Description

User-friendly framework that enables the training and the evaluation of species distribution models (SDMs). The package implements functions for data driven variable selection and model tuning and includes numerous utilities to display the results. All the functions used to select variables or to tune model hyperparameters have an interactive real-time chart displayed in the 'RStudio' viewer pane during their execution.

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Version

Install

install.packages('SDMtune')

Monthly Downloads

516

Version

1.1.0

License

GPL-3

Maintainer

Sergio Vignali

Last Published

March 11th, 2020

Functions in SDMtune (1.1.0)

Maxnet-class

Maxnet
BRT-class

Boosted Regression Tree
SDMmodelCV-class

SDMmodelCV
SDMtune-class

SDMtune class
SDMmodel-class

SDMmodel
RF-class

Random Forest
ANN-class

Artificial Neural Network
SDMmodel2MaxEnt

SDMmodel2MaxEnt
Maxent-class

Maxent
doJk

Jackknife Test
SWD-class

Sample With Data
plotCor

Plot Correlation
confMatrix

Confusion Matrix
corVar

Print Correlated Variables
plot,SDMtune,missing-method

Plot SDMtune object
maxentVarImp

Maxent Variable Importance
mergeSWD

Merge SWD Objects
plotResponse

Plot Response Curve
auc

AUC
get_tunable_args

Get Tunable Arguments
aicc

AICc
addSamplesToBg

Add Samples to Background
plotJk

Plot Jackknife Test
SDMtune-pkg

SDMtune: A package for tuning Species Distribution Models.
gridSearch

Grid Search
maxentTh

MaxEnt Thresholds
predict,RF-method

Predict RF
modelReport

Model Report
predict,Maxent-method

Predict Maxent
varImp

Variable Importance
optimizeModel

Optimize Model
predict,SDMmodel-method

Predict
plotPA

Plot Presence Absence Map
plotPred

Plot Prediction
plotROC

Plot ROC curve
prepareSWD

Prepare an SWD object
predict,BRT-method

Predict BRT
trainValTest

Train, Validation and Test datasets
predict,ANN-method

Predict ANN
tss

True Skill Statistics
randomFolds

Create Random Folds
randomSearch

Random Search
thresholds

Thresholds
reduceVar

Reduce Variables
predict,SDMmodelCV-method

Predict for Cross Validation
varSel

Variable Selection
plotVarImp

Plot Variable Importance
virtualSp

Virtual Species
swd2csv

SWD to csv
thinData

Thin Data
train

Train