BIOMOD_ModelingCV.do.full.models default value to FALSE.get_data_maskBIOMOD_Modeling when using sampsize as a vector. argument strata was badly formattedBIOMOD_EnsembleModeling for additional projection with only one environmental variablesBIOMOD_EnsembleForecasting when several projection are running simultaneously and using the same temporary directorybm_CrossValidation with user.defined tables badly formatted (TRUE/FALSE for data not in the given PA dataset are now properly transformed into NA)models.pa argument in BIOMOD_Modeling).BIOMOD_CrossValidation have been renamed bm_CrossValidation and cross-validation with k-fold, stratified and environmental strategy now work properly with pseudo-absence dataset. All cross-validation strategy can now be called directly through BIOMOD_Modeling.get_evaluations, BIOMOD_EnsembleModeling, bm_RunModelsLoop, bm_RunModel)save.output. output are now automatically saved.terra and rasterCV.perc (formerly data.split.perc) now uses a 0-1 range (instead of 0-100)BIOMOD_EnsembleModeling now gives an error.metric.select.dataset to BIOMOD_EnsembleModeling to choose the dataset which evaluation metric should be used to filter and/or weigh the ensemble models. Default value is now ‘validation’ instead of ‘evaluation’.na.rm to BIOMOD_EnsembleModeling to harmonize the management of NA among individual model predictions.RF$sampsize parameter in BIOMOD_ModelingOptionsBIOMOD_Projection and BIOMOD_EnsembleForecasting when terraOption(todisk = TRUE) is activated (for large or numerous raster).data.table object (that are converted into standard data.frame).do.stack = FALSE and resp.name with . inside.filter.raster = TRUE in bm_PseudoAbsence.BIOMOD.formated.data.PAget_species_data and get_eval_datacalib.lines and eval.lines variable names are standardised (no more calibLines or eval_lines)data.table (removed use of rbindlist).get_env_class to reduce code redundancycategorical_stack_to_terra into .categorical_stack_to_terraBIOMOD_FormatingData checks into bm_PseudoAbsencesplot and summary methods for BIOMOD_FormatingData output. These method now support the use of calib.lines to explore how the cross-validation dataset are structured.plot methods for BIOMOD.projection.out objects so that it uses ggplot2 for nicer plots.BIOMOD.projection.out objects. They can be loaded from the disk with get_predictions or represented through BIOMOD.projection.out plot method.get_predictions now return a proper data.frame (unless projection on spatial data) with many additional information available. Old behavior can be reproduced by using get_predictions(x, model.as.col = TRUE).get_evaluations now return a cleaner data.frame with more consistent information available.maxent.jar); ‘MAXENT.Phillips.2’ -> ‘MAXNET’ (based on maxnet package).BIOMOD_FormatingData now gives warning when several input data points are located in the same raster cellsfilter.raster in BIOMOD_FormatingData to filter data points so that none are located in the same raster cells.BIOMOD_EnsembleModeling now have an argument em.algo to select the ensemble algorithm to be computed. Separate arguments are now deprecated (prob.mean, prob.median, prob.cv, prob.ci, committee.averaging, prob.mean.weight). Building all possible ensemble models can now be done with em.algo = c('EMmean','EMmedian','EMcv','EMci','EMca','EMwmean').em.by have slightly changed: ‘PA_dataset’ -> ‘PA’, ‘PA_dataset+repet’ -> ‘PA+run’ and ‘PA_dataset+algo’ -> ‘PA+algo’BIOMOD_Modeling and BIOMOD_EnsembleModeling.MAXENT.Phillips.2 and single variable models.rasterVis from Suggeststidyterra and ggtext to Suggestsget_evaluation when models have no evaluations.sp is back into Imports due to the need to use sp::read.asciigridterra version number (>= 1.6-33) as terra 1.6-41 was released on CRAN.do.stack = TRUE, only stacked projection are now saved to the disk.initial_heap_size and max_heap_size in MAXENT.Phillips modeling optionsMAXENT.Phillips.MAXENT.Phillips predict method for large dataset (require sp::read.asciigrid).BIOMOD_EnsembleModeling.BIOMOD_EnsembleForecasting when a single evaluation metric was available and binary/filtered transformation were asked for.BIOMOD.formated.data.PA object.MAXENT.Phillips for Windows.do.stack = FALSE with BIOMOD_Projection.EMcv ensemble modeling for data.frame by removing dependency to raster::cv.free method with PackedSpatRasterBIOMOD_FormatingData in case where no coordinates are givenMAXENT.Phillips predict2 method for SpatRaster so that it saves environmental data as .asc and do not use the data.frame method.BIOMOD.formated.data@data.mask slot. data.mask can now be safely saved and re-opened ; data.mask can now store a different extent for evaluation datasetterra (> 1.6.33) and do not automatically import raster and sp.raster and sp package into SUGGESTS rather than DEPENDS.raster and sp input data type are still supported.data() .bm_BinaryTransformation now always returns 0/1 and never TRUE/FALSEbm_PlotResponseCurves for new.env possible data types.BIOMOD_Projection and BIOMOD_EnsembleForecasting now properly support matrix as new.envget_prediction on biomod.projection.out generated from BIOMOD_Projection based on SpatRaster with arg as.data.frame = TRUE are now possible.bm_BinaryTransformation now return same type of object as its inputBIOMOD_RangeSize, indicating how comparison are done depending on the number of models in current vs future.bm_BinaryTransformation with data.frame/matrix and do.filtering = TRUEbm_PlotResponseCurves now work with factors in univariate representationbm_PlotResponseCurves properly handles SpatRaster and Raster as new.envMAXENT.Phillips and a single environmental variableBIOMOD_EnsembleForecasting so that it properly accounts for new.env.xy when projecting on matrix or data.frame.BIOMOD_EnsembleModeling now works when called for a single ensemble modelBIOMOD_RangeSize. Comparisons with non-binary values throw errors.BIOMOD_RangeSize and data.frame methodBIOMOD_RangeSize data.frame method now handles 1 current vs n future projectionBIOMOD_PresenceOnly that can now work when evaluation data are providedBIOMOD_PresenceOnly that can now work when only the EM have been providedBIOMOD_PresenceOnly to SpatRaster and SpatVector.build_clamping_mask now support categorical variables.categorical2numeric to transform categorical variables into numeric within a data.frame..get_categorical_names to retrieve categorical variable names from a data.frame.load_stored_object method into a method for BIOMOD.stored.SpatRaster and a method for all other BIOMOD.stored.data.BIOMOD.stored.SpatRaster stores PackedSpatraster and not SpatRaster..CompteurSp based on old function CompteurSp that was defined within a function.check_data_range().BIOMOD_EnsembleForecasting.New internal function .get_kept_models to generate list of models kept by ensemble modeling depending on metric.select.
Improved checks for BIOMOD_EnsembleModel to generate warnings when ensemble models are expected to be run with <= 1 models.
repaired support for cross-validation table given as data.frame instead of matrix.
dir.name can now be provided as project argument so that results may be saved in a custom folder.CTA algorithm and categorical variables on raster is now possible.em.by = "algo" or em.by = "all") so that evaluation uses the union of PA data sets instead of the whole environmental space supplied.INT2S data format when on_0_1000 is set to TRUE.get_[...], load_stored_object and BIOMOD_LoadModels, instead of get(load(...))) and the workflow within get_[...] functions (use load_stored_object and similar arguments such as as.data.frame, full.name, …).BIOMOD.ensemble.models.out and BIOMOD.models.out objects
BIOMOD.ensemble.models.out object for evaluations, variables importance and predictions..Models.save.objects in BIOMOD_modeling to .fill_BIOMOD.models.out in biomod2_internal.R.BIOMOD.ensemble.models.out and use load_stored_object to directly get them within get_[...] functions.BIOMOD_FormatingData, instead of throwing an error linked to data.mask.on_0_1000 can now be passed without errors so that projection may either be on a range from 0 to 1 or from 0 to 1000. The latter option being more effective memory-wise.BIOMOD_EnsembleModeling so that em.by can not be of length > 1..get_models_assembling so that it did not confound MAXENT.Phillips2 with MAXENT.Phillips when grouping models by algorithm in BIOMOD_EnsembleModeling.BIOMOD.ensemble.models.out now accepts an evaluation arg. Evaluation values, variables’ importance and Calibration/Evaluation predictions for ensemble models are now properly saved by BIOMOD_EnsembleModeling().prob.ci.inf et prob.ci.sup.BIOMOD_PresenceOnly now properly manage NA.get_predictions.BIOMOD.projection.out now properly works when asked for a subset of model.gbm package to its development version at rpatin/gbm can be used. (see issue https://github.com/biomodhub/biomod2/issues/102)do.progress parameter (to render or not progress bar) and dir.name parameter in BIOMOD_FormatingData and biomod2 objects (Mathieu B. request)BIOMOD_PresenceOnly function by removing ecospat dependencyroxygen2 documentation for all functions, including examplesbiomod2_classes files)BIOMOD_FormatingData functionBIOMOD_ModelingOptions functionBIOMOD_FormatingData : test class condition only a first element (to deal with matrix / array objects)BIOMOD_EnsembleForecasting for EMcv model when only one single model was keptBIOMOD_PresenceOnly function (previously BIOMOD_presenceonly)BIOMOD_CrossValidation function (previously BIOMOD_cv)MinMax values, when factor included : should get clamping mask to workget_predictions function for ensemble modelsearth package (was mda in previous versions)formulaBIOMOD_tuning function (Frank B.)betamultiplier parameter to tune MAXENT.Phillips (Frank B. request)MAXENT.Phillips with proper background dataMAXENT has been renamed MAXENT.PhillipsMAXENT.Tsuruokabiomod2 (Frank B. contribution)BIOMOD_cv to control models cross validation procedureBIOMOD_presenceonly to evaluate biomod models using boyce and mpa indicesBIOMOD_tuning to automatically tune BIOMOD_ModelingOptions parametersggplot2as.data.frame argument for get_evaluations() function to enable formal and ensemble models evaluation scores mergingMAXENT calculations (via java) (thanks to Burke G.)do.stack argument is set to FALSEbiomod2 objects from a version to the current oneFALSE by defaultbiomod2 models objects (should be predicted, evaluated, and you can do variables importance) the same way than all formal biomod2 modelsbiomod2_projection object: should be plotted…variable_importance functionmgcv to gam to deal with memory (cache) over-consuming (thanks to Burke G.)response.plot2 function (optimization + deal with factorial variables)ProbDensFunc() function to package to produce nice plots that show inter-models variabilityrasterVis dependency for nicer biomod2 plotsPA.dist.min and PA.dist.max are now defined in meters when you work with unprojected rasters in disk pseudo absences selectionBIOMOD_Modelingcol, lty, data_species…)modeling.id arg (BIOMOD_Modeling) for prevent from no wanted models overwriting and facilitate models tests and comparisons (thanks Frank B.)biomod2 datasetpROC package dependencyRemoveProperly()BIOMOD_Projection outputsBIOMOD_LoadModels supports multiple models inputNA in evaluation table issue (*thanks Frank B.)MAXENT categorical variables and categorical raster inputbiomod2 are now defined as “biomod2 models objects” (own scaling models, own predict function, …).grd or .img)