OptionsBigboss
have been
modified (concern only ANN, CTA and RF models)bm_pseudoAbsences
and
BIOMOD_FormatingData
.bm_ModelingOptions
.BIOMOD_EnsembleModeling
when multiple PA datasets
(obs
and fit
not matching when calling
bm_FindOptimStat
)new.env
in BIOMOD_Projection
and
BIOMOD_EnsembleForecasting
tests/
folder (unused)new.env
in
BIOMOD_Projection
and
BIOMOD_EnsembleForecasting
BIOMOD.options.dataset
and
BIOMOD.models.options
classesGAM.binary.gam.gam
and
GAM.binary.mgcv.bam
in OptionsBigboss
datasetobs
and fit
parameters in
bm_FindOptimStat
bm_Tuning
function :
MAXENT.algorithm
and MAXENT.parallel
parametersXGBOOST
and SRE
gamSpline
to gamLoess
method
to tune GAM.gam.gam
model, and add GAM.span
and GAM.degree
parametersBIOMOD_Modeling
BIOMOD.options.default
and
BIOMOD.options.dataset
classes, retrieving default
parameters and values with formalArgs
functionModelsTable
and OptionsBigboss
datasets containing single models informations and pre-defined modeling
optionsBIOMOD_ModelingOptions
to
bm_ModelingOptions
bm_ModelingOptions
directly in
BIOMOD_Modeling
and add related OPT.[...]
parametersBIOMOD_Tuning
to bm_Tuning
and adapt
it to match with new modeling optionsbm_RunModelsLoop
in
a more generalized way dealing with new modeling optionsBIOMOD_PresenceOnly
function and add
BOYCE
and MPA
indices into
bm_FindOptimStat
functionFLT4S
datatype only when
EMcv is activated, otherwise use INT2S
wrap
applied to a
data.frame
in BIOMOD_Projection
predict
method for RF
with
do.classif = FALSE
bm_PlotEvalMean
do.classif
ignored in
BIOMOD_ModelingOptions
BIOMOD_Projection
BIOMOD.formated.data
and BIOMOD.formated.data.PA
bm_PlotResponseCurves
for ensemble
models merged by algo (for Maxent)point.size
argument to
plot.BIOMOD.Formated.data
maxcell
argument to
plot.BIOMOD.projected.out
verbose = 0
(from
verbose = 1
)BIOMOD_FormatingData
checks for
resp.xy
.get_data_mask
BIOMOD_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 raster
CV.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_ModelingOptions
BIOMOD_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.PA
get_species_data
and
get_eval_data
.BIOMOD_Modeling.prepare.data
bm_RunModelsLoop
to do the PA loop within
the functioncalib.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_terra
BIOMOD_FormatingData
checks into
bm_PseudoAbsences
'.tif'
is available as an output format for raster
projection'.tif'
is the new default output format for raster
projectionplot
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.BIOMOD_CrossValidation
for block-stratified
samplingBIOMOD_CrossValidation
for
pseudo-absencessp
is back into Imports
due to the
need to use sp::read.asciigrid
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.initial_heap_size
and max_heap_size
in MAXENT.Phillips
modeling optionsMAXENT.Phillips
.MAXENT.Phillips
predict method for large dataset
(require sp::read.asciigrid
).em.by = 'all'
or 'algo'
.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
PackedSpatRaster
BIOMOD_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
/FALSE
bm_PlotResponseCurves
for
new.env
possible data types.BIOMOD_Projection
and
BIOMOD_EnsembleForecasting
now properly support matrix as
new.env
get_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.BIOMOD_CrossValidation
bm_BinaryTransformation
with
data.frame
/matrix
and
do.filtering = TRUE
bm_PlotResponseCurves
now work with factors in
univariate representationbm_PlotResponseCurves
properly handles
SpatRaster
and Raster
as
new.env
MAXENT.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
.metric.select
.BIOMOD_EnsembleModeling
to generate
warnings when ensemble models are expected to be run with <= 1
models.data.frame
instead of matrix
.dir.name
can now be provided as project argument so
that results may be saved in a custom folder.predict
with 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
.get_predictions
method for
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
.bm_PlotResponseCurves
to only plot
show.variables
.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)biomod2_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 keptbetamultiplier
parameter to tune MAXENT.Phillips
(Frank B. request)as.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 FALSE
biomod2
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_Modeling
col
,
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_LoadModels
supports multiple models inputNA
in evaluation table issue (*thanks
Frank B.)biomod2
are now defined as “biomod2
models objects” (own scaling models, own predict function, …).grd
or .img
)