CV.do.full.models default value to
BIOMOD_Modeling when using
sampsize as a vector. argument
strata was badly formatted
BIOMOD_EnsembleModeling for additional projection with only one environmental variables
BIOMOD_EnsembleForecasting when several projection are running simultaneously and using the same temporary directory
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_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
save.output. output are now automatically saved.
data.split.perc) now uses a 0-1 range (instead of 0-100)
BIOMOD_EnsembleModeling now gives an error.
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’.
BIOMOD_EnsembleModeling to harmonize the management of
NA among individual model predictions.
RF$sampsize parameter in
terraOption(todisk = TRUE) is activated (for large or numerous raster).
data.table object (that are converted into standard
do.stack = FALSE and
filter.raster = TRUE in
eval.lines variable names are standardised (no more
data.table (removed use of
.get_env_class to reduce code redundancy
BIOMOD_FormatingData checks into
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
BIOMOD_FormatingData now gives warning when several input data points are located in the same raster cells
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’
MAXENT.Phillips.2 and single variable models.
get_evaluation when models have no evaluations.
sp is back into
Imports due to the need to use
terra 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.
MAXENT.Phillips modeling options
MAXENT.Phillips predict method for large dataset (require
BIOMOD_EnsembleForecasting when a single evaluation metric was available and binary/filtered transformation were asked for.
MAXENT.Phillips for Windows.
do.stack = FALSE with
EMcv ensemble modeling for
data.frame by removing dependency to
free method with
BIOMOD_FormatingData in case where no coordinates are given
predict2 method for
SpatRaster so that it saves environmental data as
.asc and do not use the
data.mask can now be safely saved and re-opened ;
data.mask can now store a different extent for evaluation dataset
> 1.6.33) and do not automatically import
sp package into
SUGGESTS rather than
sp input data type are still supported.
bm_BinaryTransformation now always returns
1 and never
new.env possible data types.
BIOMOD_EnsembleForecasting now properly support matrix as
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 input
BIOMOD_RangeSize, indicating how comparison are done depending on the number of models in current vs future.
do.filtering = TRUE
bm_PlotResponseCurves now work with factors in univariate representation
bm_PlotResponseCurves properly handles
MAXENT.Phillips and a single environmental variable
BIOMOD_EnsembleForecasting so that it properly accounts for
new.env.xy when projecting on
BIOMOD_EnsembleModeling now works when called for a single ensemble model
BIOMOD_RangeSize. Comparisons with non-binary values throw errors.
BIOMOD_RangeSize and data.frame method
data.frame method now handles 1 current vs n future projection
BIOMOD_PresenceOnly that can now work when evaluation data are provided
BIOMOD_PresenceOnly that can now work when only the EM have been provided
build_clamping_mask now support categorical variables
.categorical2numeric to transform categorical variables into numeric within a
.get_categorical_names to retrieve categorical variable names from a
load_stored_object method into a method for
BIOMOD.stored.SpatRaster and a method for all other
PackedSpatraster and not
.CompteurSp based on old function CompteurSp that was defined within a function.
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
BIOMOD_LoadModels, instead of
get(load(...))) and the workflow within
get_[...] functions (use
load_stored_object and similar arguments such as
BIOMOD.ensemble.models.out object for evaluations, variables importance and predictions.
BIOMOD.ensemble.models.out and use
load_stored_object to directly get them within
BIOMOD_FormatingData, instead of throwing an error linked to
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.Phillips when grouping models by algorithm in
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_PresenceOnly now properly manage
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
biomod2 objects (Mathieu B. request)
BIOMOD_PresenceOnly function by removing
roxygen2 documentation for all functions, including examples
BIOMOD_FormatingData : test class condition only a first element (to deal with
EMcv model when only one single model was kept
BIOMOD_PresenceOnly function (previously
BIOMOD_CrossValidation function (previously
MinMax values, when factor included : should get clamping mask to work
get_predictions function for ensemble models
earth package (was
mda in previous versions)
BIOMOD_tuning function (Frank B.)
betamultiplier parameter to tune MAXENT.Phillips (Frank B. request)
MAXENT.Phillips with proper background data
MAXENT has been renamed
biomod2 (Frank B. contribution)
BIOMOD_cv to control models cross validation procedure
BIOMOD_presenceonly to evaluate biomod models using boyce and mpa indices
BIOMOD_tuning to automatically tune
as.data.frame argument for
get_evaluations() function to enable formal and ensemble models evaluation scores merging
MAXENT calculations (via java) (thanks to Burke G.)
do.stack argument is set to
biomod2 objects from a version to the current one
FALSE by default
biomod2 models objects (should be predicted, evaluated, and you can do variables importance) the same way than all formal
biomod2_projection object: should be plotted…
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 variability
rasterVis dependency for nicer
PA.dist.max are now defined in meters when you work with unprojected rasters in disk pseudo absences selection
modeling.id arg (
BIOMOD_Modeling) for prevent from no wanted models overwriting and facilitate models tests and comparisons (thanks Frank B.)
pROC package dependency
BIOMOD_LoadModels supports multiple models input
NA in evaluation table issue (*thanks Frank B.)
MAXENT categorical variables and categorical raster input
biomod2 are now defined as “biomod2 models objects” (own scaling models, own predict function, …)