Bug fixes: Add the argument
factors to the function
modelReport() to allow predictions for raster objects including categorical variables
checkMaxentInstallation()to check if Maxent is correctly configured.
get_tunable_args()has been replaced by
getTunableArgs()to be consistent with the camel case function naming. At the moment is still possible to use both functions but
get_tunable_args()will be deprecated in the next release.
plotPA function now works also with new version of
thinDataaccept now a matrix or a dataframe with several columns, useful if the users has information related to the coordinates that doesn’t want to lose with the thinning procedure.
plotResponseplots the response for the full range of presences and backgrounds/absences when only_presence is TRUE, only_presence is unused only to compute the provided function to the range of presence locations when marginal = TRUE
addSamplesToBgto add presence locations to background location.
extra_argsin Maxent models cannot be changed anymore.
Bug fix: Fix CRAN error for r-oldrel
plotPAfunction now works also with large raster objects
This release is the first step to enable more methods to train models. The main change is that the
SDW object now bundles together the presence and the absence/background locations and is not necessary anymore to pass the presence and absence locations as separate arguments to the
Old objects of class
SDMtune created with version <= 0.1.1 must be converted into the new format using the dedicated help functions. Please check the article Deprecated objects in the package website.
SWDobject bundles together the presence and absence/background locations
trainaccepts now folds partition generated with other packages (i.e. ENMeval and blockCV)
virtualSpwith presence, absence and background locations
maxentVarImpfunction available for