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:
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')) |
Bug tracker:https://github.com/consbiol-unibern/sdmtune/issues
- virtualSp - Virtual Species
hyperparameter-tuningspecies-distribution-modellingvariable-selection
Last updated 1 years agofrom:46605d19a6. Checks:OK: 7 NOTE: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 12 2024 |
| R-4.5-win-x86_64 | NOTE | Nov 12 2024 |
| R-4.5-linux-x86_64 | NOTE | Nov 12 2024 |
| R-4.4-win-x86_64 | OK | Nov 12 2024 |
| R-4.4-mac-x86_64 | OK | Nov 12 2024 |
| R-4.4-mac-aarch64 | OK | Nov 12 2024 |
| R-4.3-win-x86_64 | OK | Nov 12 2024 |
| R-4.3-mac-x86_64 | OK | Nov 12 2024 |
| R-4.3-mac-aarch64 | OK | Nov 12 2024 |
Exports:addSamplesToBgaiccANNaucBRTcheckMaxentInstallationcombineCVconfMatrixcorVardoJkgetTunableArgsgridSearchMaxentmaxentThmaxentVarImpMaxnetmergeSWDmodelReportoptimizeModelplotplotCorplotJkplotPAplotPredplotResponseplotROCplotVarImppredictprepareSWDrandomFoldsrandomSearchreduceVarRFSDMmodelSDMmodel2MaxEntSDMmodelCVSDMtuneSWDswd2csvthinDatathresholdstraintrainValTesttssvarImpvarSel
Dependencies:clicodetoolscolorspacedismofansifarverforeachgbmggplot2glmnetgluegtableisobanditeratorsjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmaxnetmgcvmunsellnlmennetpillarpkgconfigR6randomForestrasterRColorBrewerRcppRcppEigenrlangrstudioapiscalesshapespstringistringrsurvivalterratibbleutf8vctrsviridisLitewhiskerwithr
SDMtune - basic use
Rendered frombasic-use.Rmdusingknitr::rmarkdownon Nov 12 2024.Last update: 2023-05-01
Started: 2020-06-09
SDMtune - hyperparameter tuning
Rendered fromhyper-tuning.Rmdusingknitr::rmarkdownon Nov 12 2024.Last update: 2023-05-01
Started: 2020-06-09
SDMtune - presence absence models
Rendered frompresence-absence.Rmdusingknitr::rmarkdownon Nov 12 2024.Last update: 2023-05-01
Started: 2020-06-09
SDMtune - variable selection
Rendered fromvar-selection.Rmdusingknitr::rmarkdownon Nov 12 2024.Last update: 2023-05-01
Started: 2020-06-09
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Add Samples to Background | addSamplesToBg |
| AICc | aicc |
| Artificial Neural Network | ANN ANN-class show,ANN-method |
| AUC | auc |
| Boosted Regression Tree | BRT BRT-class show,BRT-method |
| Check Maxent Installation | checkMaxentInstallation |
| Combine Cross Validation models | combineCV |
| Confusion Matrix | confMatrix |
| Print Correlated Variables | corVar |
| Jackknife Test | doJk |
| Get Tunable Arguments | getTunableArgs |
| Grid Search | gridSearch |
| Maxent | Maxent Maxent-class show,Maxent-method |
| MaxEnt Thresholds | maxentTh |
| Maxent Variable Importance | maxentVarImp |
| Maxnet | Maxnet Maxnet-class show,Maxnet-method |
| Merge SWD Objects | mergeSWD |
| Model Report | modelReport |
| Optimize Model | optimizeModel |
| Plot Correlation | plotCor |
| Plot Jackknife Test | plotJk |
| Plot Presence Absence Map | plotPA |
| Plot Prediction | plotPred |
| Plot Response Curve | plotResponse |
| Plot ROC curve | plotROC |
| Plot Variable Importance | plotVarImp |
| Predict ANN | predict,ANN-method |
| Predict BRT | predict,BRT-method |
| Predict Maxent | predict,Maxent-method |
| Predict Maxnet | predict,Maxnet-method |
| Predict RF | predict,RF-method |
| Predict | predict,SDMmodel-method |
| Predict for Cross Validation | predict,SDMmodelCV-method |
| Prepare an SWD object | prepareSWD |
| Create Random Folds | randomFolds |
| Random Search | randomSearch |
| Reduce Variables | reduceVar |
| Random Forest | RF RF-class show,RF-method |
| SDMmodel | SDMmodel SDMmodel-class show,SDMmodel-method |
| SDMmodel2MaxEnt | SDMmodel2MaxEnt |
| SDMmodelCV | SDMmodelCV SDMmodelCV-class show,SDMmodelCV-method |
| SDMtune class | plot,SDMtune,missing-method SDMtune SDMtune-class show,SDMtune-method |
| Sample With Data | show,SWD-method SWD SWD-class |
| SWD to csv | swd2csv |
| Thin Data | thinData |
| Thresholds | thresholds |
| Train | train |
| Train, Validation and Test datasets | trainValTest |
| True Skill Statistics | tss |
| Variable Importance | varImp |
| Variable Selection | varSel |
| Virtual Species | virtualSp |
