Package: SDMtune 1.3.1

Sergio Vignali

SDMtune: Species Distribution Model Selection

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.

Authors:Sergio Vignali [aut, cre], Arnaud Barras [aut], Veronika Braunisch [aut], Conservation Biology - University of Bern [fnd]

SDMtune_1.3.1.tar.gz
SDMtune_1.3.1.zip(r-4.5)SDMtune_1.3.1.zip(r-4.4)SDMtune_1.3.1.zip(r-4.3)
SDMtune_1.3.1.tgz(r-4.4-x86_64)SDMtune_1.3.1.tgz(r-4.4-arm64)SDMtune_1.3.1.tgz(r-4.3-x86_64)SDMtune_1.3.1.tgz(r-4.3-arm64)
SDMtune_1.3.1.tar.gz(r-4.5-noble)SDMtune_1.3.1.tar.gz(r-4.4-noble)
SDMtune_1.3.1.tgz(r-4.4-emscripten)SDMtune_1.3.1.tgz(r-4.3-emscripten)
SDMtune.pdf |SDMtune.html
SDMtune/json (API)
NEWS

# Install 'SDMtune' in R:
install.packages('SDMtune', repos = c('https://consbiol-unibern.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/consbiol-unibern/sdmtune/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

hyperparameter-tuningspecies-distribution-modellingvariable-selection

7.19 score 23 stars 113 scripts 430 downloads 3 mentions 46 exports 49 dependencies

Last updated 1 years agofrom:46605d19a6. Checks:OK: 7 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 13 2024
R-4.5-win-x86_64NOTEOct 13 2024
R-4.5-linux-x86_64NOTEOct 13 2024
R-4.4-win-x86_64OKOct 13 2024
R-4.4-mac-x86_64OKOct 13 2024
R-4.4-mac-aarch64OKOct 13 2024
R-4.3-win-x86_64OKOct 13 2024
R-4.3-mac-x86_64OKOct 13 2024
R-4.3-mac-aarch64OKOct 13 2024

Exports:addSamplesToBgaiccANNaucBRTcheckMaxentInstallationcombineCVconfMatrixcorVardoJkgetTunableArgsgridSearchMaxentmaxentThmaxentVarImpMaxnetmergeSWDmodelReportoptimizeModelplotplotCorplotJkplotPAplotPredplotResponseplotROCplotVarImppredictprepareSWDrandomFoldsrandomSearchreduceVarRFSDMmodelSDMmodel2MaxEntSDMmodelCVSDMtuneSWDswd2csvthinDatathresholdstraintrainValTesttssvarImpvarSel

Dependencies:clicodetoolscolorspacedismofansifarverforeachgbmggplot2glmnetgluegtableisobanditeratorsjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmaxnetmgcvmunsellnlmennetpillarpkgconfigR6randomForestrasterRColorBrewerRcppRcppEigenrlangrstudioapiscalesshapespstringistringrsurvivalterratibbleutf8vctrsviridisLitewhiskerwithr

SDMtune - basic use

Rendered frombasic-use.Rmdusingknitr::rmarkdownon Oct 13 2024.

Last update: 2023-05-01
Started: 2020-06-09

SDMtune - hyperparameter tuning

Rendered fromhyper-tuning.Rmdusingknitr::rmarkdownon Oct 13 2024.

Last update: 2023-05-01
Started: 2020-06-09

SDMtune - presence absence models

Rendered frompresence-absence.Rmdusingknitr::rmarkdownon Oct 13 2024.

Last update: 2023-05-01
Started: 2020-06-09

SDMtune - variable selection

Rendered fromvar-selection.Rmdusingknitr::rmarkdownon Oct 13 2024.

Last update: 2023-05-01
Started: 2020-06-09

Readme and manuals

Help Manual

Help pageTopics
Add Samples to BackgroundaddSamplesToBg
AICcaicc
Artificial Neural NetworkANN ANN-class show,ANN-method
AUCauc
Boosted Regression TreeBRT BRT-class show,BRT-method
Check Maxent InstallationcheckMaxentInstallation
Combine Cross Validation modelscombineCV
Confusion MatrixconfMatrix
Print Correlated VariablescorVar
Jackknife TestdoJk
Get Tunable ArgumentsgetTunableArgs
Grid SearchgridSearch
MaxentMaxent Maxent-class show,Maxent-method
MaxEnt ThresholdsmaxentTh
Maxent Variable ImportancemaxentVarImp
MaxnetMaxnet Maxnet-class show,Maxnet-method
Merge SWD ObjectsmergeSWD
Model ReportmodelReport
Optimize ModeloptimizeModel
Plot CorrelationplotCor
Plot Jackknife TestplotJk
Plot Presence Absence MapplotPA
Plot PredictionplotPred
Plot Response CurveplotResponse
Plot ROC curveplotROC
Plot Variable ImportanceplotVarImp
Predict ANNpredict,ANN-method
Predict BRTpredict,BRT-method
Predict Maxentpredict,Maxent-method
Predict Maxnetpredict,Maxnet-method
Predict RFpredict,RF-method
Predictpredict,SDMmodel-method
Predict for Cross Validationpredict,SDMmodelCV-method
Prepare an SWD objectprepareSWD
Create Random FoldsrandomFolds
Random SearchrandomSearch
Reduce VariablesreduceVar
Random ForestRF RF-class show,RF-method
SDMmodelSDMmodel SDMmodel-class show,SDMmodel-method
SDMmodel2MaxEntSDMmodel2MaxEnt
SDMmodelCVSDMmodelCV SDMmodelCV-class show,SDMmodelCV-method
SDMtune classplot,SDMtune,missing-method SDMtune SDMtune-class show,SDMtune-method
Sample With Datashow,SWD-method SWD SWD-class
SWD to csvswd2csv
Thin DatathinData
Thresholdsthresholds
Traintrain
Train, Validation and Test datasetstrainValTest
True Skill Statisticstss
Variable ImportancevarImp
Variable SelectionvarSel
Virtual SpeciesvirtualSp