Package: biomod2 4.2-6-1
biomod2: Ensemble Platform for Species Distribution Modeling
Functions for species distribution modeling, calibration and evaluation, ensemble of models, ensemble forecasting and visualization. The package permits to run consistently up to 10 single models on a presence/absences (resp presences/pseudo-absences) dataset and to combine them in ensemble models and ensemble projections. Some bench of other evaluation and visualization tools are also available within the package.
Authors:
biomod2_4.2-6-1.tar.gz
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biomod2_4.2-6-1.tgz(r-4.4-any)biomod2_4.2-6-1.tgz(r-4.3-any)
biomod2_4.2-6-1.tar.gz(r-4.5-noble)biomod2_4.2-6-1.tar.gz(r-4.4-noble)
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biomod2.pdf |biomod2.html✨
biomod2/json (API)
# Install 'biomod2' in R: |
install.packages('biomod2', repos = c('https://biomodhub.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/biomodhub/biomod2/issues
- DataSpecies - Presence-Absence data to build test SDM
- ModelsTable - Single models package and functions
- OptionsBigboss - Bigboss pre-defined parameter values for single models
- bioclim_current - Bioclimatic variables for SDM based on current condition
- bioclim_future - Bioclimatic variables for SDM based on future condition
Last updated 13 days agofrom:3e3561c403. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 24 2024 |
R-4.5-win | OK | Oct 24 2024 |
R-4.5-linux | OK | Oct 24 2024 |
R-4.4-win | OK | Oct 24 2024 |
R-4.4-mac | OK | Oct 24 2024 |
R-4.3-win | OK | Oct 24 2024 |
R-4.3-mac | OK | Oct 24 2024 |
Exports:.transform_model.as.col.transform.outputs.listBinaryTransformationBIOMOD_CrossValidationBIOMOD_EnsembleForecastingBIOMOD_EnsembleModelingBIOMOD_FormatingDataBIOMOD_LoadModelsBIOMOD_ModelingBIOMOD_ModelingOptionsBIOMOD_PresenceOnlyBIOMOD_ProjectionBIOMOD_RangeSizeBIOMOD_TuningBIOMOD.formated.dataBIOMOD.formated.data.PABIOMOD.options.datasetBIOMOD.options.defaultbm_BinaryTransformationbm_CalculateStatbm_CrossValidationbm_CrossValidation_blockbm_CrossValidation_envbm_CrossValidation_kfoldbm_CrossValidation_randombm_CrossValidation_stratbm_CrossValidation_user.definedbm_FindOptimStatbm_MakeFormulabm_ModelingOptionsbm_PlotEvalBoxplotbm_PlotEvalMeanbm_PlotRangeSizebm_PlotResponseCurvesbm_PlotVarImpBoxplotbm_PseudoAbsencesbm_PseudoAbsences_diskbm_PseudoAbsences_randombm_PseudoAbsences_srebm_PseudoAbsences_user.definedbm_RunModelbm_RunModelsLoopbm_SampleBinaryVectorbm_SampleFactorLevelsbm_SREbm_Tuningbm_VariablesImportancecalculate.statFind.Optim.Statfreeget_built_modelsget_calib_linesget_eval_dataget_evaluationsget_formal_dataget_formal_modelget_kept_modelsget_optim_valueget_optionsget_predictionsget_projected_modelsget_scaling_modelget_species_dataget_variables_importancegetStatOptimValueload_stored_objectmakeFormulamodels_scores_graphplotpredictprintProbDensFuncresponse.plot2sample.factor.levelsshowsresummaryvariables_importancezzz_bm
Dependencies:abindclicodetoolscolorspacedplyrfansifarverforeachgbmgenericsggplot2gluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrPresenceAbsencepROCR6RColorBrewerRcppreshapereshape2rlangrpartscalesspstringistringrsurvivalterratibbletidyselectutf8vctrsviridisLitewithr
Cross-validation
Rendered fromvignette_crossValidation.Rmd
usingknitr::knitr
on Oct 24 2024.Last update: 2024-09-12
Started: 2024-02-14
Data preparation
Rendered fromvignette_dataPreparation.Rmd
usingknitr::knitr
on Oct 24 2024.Last update: 2024-09-12
Started: 2023-09-12
Main functions
Rendered fromexamples_1_mainFunctions.Rmd
usingknitr::knitr
on Oct 24 2024.Last update: 2024-09-12
Started: 2022-03-01
Modeling Options
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usingknitr::knitr
on Oct 24 2024.Last update: 2024-09-02
Started: 2024-02-14
News
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usingknitr::knitr
on Oct 24 2024.Last update: 2024-09-02
Started: 2022-03-01
Presentation
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usingknitr::knitr
on Oct 24 2024.Last update: 2024-09-12
Started: 2024-05-06
Pseudo-absences
Rendered fromvignette_pseudoAbsences.Rmd
usingknitr::knitr
on Oct 24 2024.Last update: 2024-09-12
Started: 2022-03-01
Secondary functions
Rendered fromexamples_2_secundaryFunctions.Rmd
usingknitr::knitr
on Oct 24 2024.Last update: 2024-09-12
Started: 2022-03-01
Variability in results
Rendered fromvignette_variability.Rmd
usingknitr::knitr
on Oct 24 2024.Last update: 2024-09-12
Started: 2022-07-13
Videos
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usingknitr::knitr
on Oct 24 2024.Last update: 2024-02-15
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Vignette Abundance
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usingknitr::knitr
on Oct 24 2024.Last update: 2024-10-24
Started: 2024-09-02
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Bioclimatic variables for SDM based on current condition | bioclim_current |
Bioclimatic variables for SDM based on future condition | bioclim_future |
Project ensemble species distribution models onto new environment | BIOMOD_EnsembleForecasting |
Create and evaluate an ensemble set of models and predictions | BIOMOD_EnsembleModeling |
Format input data, and select pseudo-absences if wanted, for usage in 'biomod2' | BIOMOD_FormatingData |
Load species distribution models built with 'biomod2' | BIOMOD_LoadModels |
Run a range of species distribution models | BIOMOD_Modeling |
Project a range of calibrated species distribution models onto new environment | BIOMOD_Projection |
Analyze the range size differences between projections of species distribution models | BIOMOD_RangeSize BIOMOD_RangeSize,data.frame,data.frame-method BIOMOD_RangeSize,SpatRaster,SpatRaster-method |
'BIOMOD_EnsembleModeling()' output object class | BIOMOD.ensemble.models.out BIOMOD.ensemble.models.out-class show,BIOMOD.ensemble.models.out-method |
'BIOMOD_FormatingData()' output object class | BIOMOD.formated.data BIOMOD.formated.data,data.frame,ANY-method BIOMOD.formated.data,numeric,data.frame-method BIOMOD.formated.data,numeric,matrix-method BIOMOD.formated.data,numeric,SpatRaster-method BIOMOD.formated.data-class show,BIOMOD.formated.data-method |
'BIOMOD_FormatingData()' output object class (with pseudo-absences) | BIOMOD.formated.data.PA BIOMOD.formated.data.PA,numeric,data.frame-method BIOMOD.formated.data.PA,numeric,SpatRaster-method BIOMOD.formated.data.PA-class |
'bm_ModelingOptions' output object class | BIOMOD.models.options BIOMOD.models.options-class print,BIOMOD.models.options-method show,BIOMOD.models.options-method |
'BIOMOD_Modeling()' output object class | BIOMOD.models.out BIOMOD.models.out-class show,BIOMOD.models.out-method |
'bm_ModelingOptions' output object class | BIOMOD.options.dataset BIOMOD.options.dataset,character-method BIOMOD.options.dataset-class print,BIOMOD.options.dataset-method show,BIOMOD.options.dataset-method |
'bm_ModelingOptions' output object class | BIOMOD.options.default BIOMOD.options.default,character,character-method BIOMOD.options.default-class |
'BIOMOD_Projection()' output object class | BIOMOD.projection.out BIOMOD.projection.out-class plot,BIOMOD.projection.out,missing-method show,BIOMOD.projection.out-method |
'BIOMOD_Modeling' and 'BIOMOD_EnsembleModeling' output object class | BIOMOD.stored.data BIOMOD.stored.data-class BIOMOD.stored.data.frame-class BIOMOD.stored.files-class BIOMOD.stored.formated.data-class BIOMOD.stored.models.out-class BIOMOD.stored.options-class BIOMOD.stored.SpatRaster-class |
Ensemble model output object class (when running 'BIOMOD_EnsembleModeling()') | biomod2_ensemble_model biomod2_ensemble_model-class EMca_biomod2_model-class EMci_biomod2_model-class EMcv_biomod2_model-class EMmean_biomod2_model-class EMmedian_biomod2_model-class EMwmean_biomod2_model-class show,biomod2_ensemble_model-method |
Single model output object class (when running 'BIOMOD_Modeling()') | ANN_biomod2_model-class biomod2_model biomod2_model-class CTA_biomod2_model-class FDA_biomod2_model-class GAM_biomod2_model-class GBM_biomod2_model-class GLM_biomod2_model-class MARS_biomod2_model-class MAXENT_biomod2_model-class MAXNET_biomod2_model-class RFd_biomod2_model-class RF_biomod2_model-class show,biomod2_model-method SRE_biomod2_model-class XGBOOST_biomod2_model-class |
Convert probability values into binary values using a predefined threshold | bm_BinaryTransformation bm_BinaryTransformation,data.frame-method bm_BinaryTransformation,matrix-method bm_BinaryTransformation,numeric-method bm_BinaryTransformation,SpatRaster-method |
Build cross-validation table | bm_CrossValidation bm_CrossValidation_block bm_CrossValidation_block,BIOMOD.formated.data-method bm_CrossValidation_block,BIOMOD.formated.data.PA-method bm_CrossValidation_env bm_CrossValidation_env,BIOMOD.formated.data-method bm_CrossValidation_env,BIOMOD.formated.data.PA-method bm_CrossValidation_kfold bm_CrossValidation_kfold,BIOMOD.formated.data-method bm_CrossValidation_kfold,BIOMOD.formated.data.PA-method bm_CrossValidation_random bm_CrossValidation_random,BIOMOD.formated.data-method bm_CrossValidation_random,BIOMOD.formated.data.PA-method bm_CrossValidation_strat bm_CrossValidation_strat,BIOMOD.formated.data-method bm_CrossValidation_strat,BIOMOD.formated.data.PA-method bm_CrossValidation_user.defined bm_CrossValidation_user.defined,BIOMOD.formated.data-method bm_CrossValidation_user.defined,BIOMOD.formated.data.PA-method |
Calculate the best score according to a given evaluation method | bm_CalculateStat bm_FindOptimStat get_optim_value |
Standardized formula maker | bm_MakeFormula |
Configure the modeling options for each selected model | bm_ModelingOptions |
Plot boxplot of evaluation scores | bm_PlotEvalBoxplot |
Plot mean evaluation scores | bm_PlotEvalMean |
Plot species range change | bm_PlotRangeSize |
Plot response curves | bm_PlotResponseCurves |
Plot boxplot of variables importance | bm_PlotVarImpBoxplot |
Select pseudo-absences | bm_PseudoAbsences bm_PseudoAbsences_disk bm_PseudoAbsences_disk,ANY,SpatRaster-method bm_PseudoAbsences_disk,ANY,SpatVector-method bm_PseudoAbsences_random bm_PseudoAbsences_random,ANY,SpatRaster-method bm_PseudoAbsences_random,ANY,SpatVector-method bm_PseudoAbsences_sre bm_PseudoAbsences_sre,ANY,SpatRaster-method bm_PseudoAbsences_sre,ANY,SpatVector-method bm_PseudoAbsences_user.defined bm_PseudoAbsences_user.defined,ANY,SpatRaster-method bm_PseudoAbsences_user.defined,ANY,SpatVector-method |
Loop to compute all single species distribution models | bm_RunModel bm_RunModelsLoop |
Sample binary vector | bm_SampleBinaryVector |
Sample all levels of a factorial variable | bm_SampleFactorLevels bm_SampleFactorLevels.data.frame bm_SampleFactorLevels.raster |
Surface Range Envelope | bm_SRE |
Tune models parameters | bm_Tuning |
Variables' importance calculation | bm_VariablesImportance |
Presence-Absence data to build test SDM | DataSpecies |
Functions to extract informations from 'biomod2_model' objects | getters.bm get_formal_model get_formal_model,biomod2_model-method get_scaling_model get_scaling_model,biomod2_model-method |
Functions to extract informations from 'BIOMOD.models.out', 'BIOMOD.projection.out' or 'BIOMOD.ensemble.models.out' objects | free free,BIOMOD.projection.out-method getters.out get_built_models get_built_models,BIOMOD.ensemble.models.out-method get_built_models,BIOMOD.models.out-method get_calib_lines get_calib_lines,BIOMOD.models.out-method get_evaluations get_evaluations,BIOMOD.ensemble.models.out-method get_evaluations,BIOMOD.models.out-method get_eval_data get_eval_data,BIOMOD.formated.data-method get_formal_data get_formal_data,BIOMOD.ensemble.models.out-method get_formal_data,BIOMOD.models.out-method get_kept_models get_kept_models,BIOMOD.ensemble.models.out-method get_options get_options,BIOMOD.models.out-method get_predictions get_predictions,BIOMOD.ensemble.models.out-method get_predictions,BIOMOD.models.out-method get_predictions,BIOMOD.projection.out-method get_projected_models get_projected_models,BIOMOD.projection.out-method get_species_data get_species_data,BIOMOD.formated.data-method get_species_data,BIOMOD.formated.data.PA-method get_variables_importance get_variables_importance,BIOMOD.ensemble.models.out-method get_variables_importance,BIOMOD.models.out-method |
Functions to load 'BIOMOD.stored.data' objects | load_stored_object load_stored_object,BIOMOD.stored.data-method load_stored_object,BIOMOD.stored.SpatRaster-method |
Single models package and functions | ModelsTable |
Bigboss pre-defined parameter values for single models | OptionsBigboss |
'plot' method for 'BIOMOD.formated.data' object class | plot,BIOMOD.formated.data,missing-method |
Functions to get predictions from 'biomod2_model' objects | predict,biomod2_model-method predict.biomod2_model predict.bm |
Functions to get predictions from 'biomod2_ensemble_model' objects | predict.em |
'summary' method for 'BIOMOD.formated.data' object class | summary,BIOMOD.formated.data-method |