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tune_max: a grid combination whose fits all failed was
assigned the previous combination’s model/performance (stale
mod); now initialised to NULL and checked with
is.null() (#478)fit_max: the final refit fallback used the CV loop
index in its check and could discard a successful fit; it now uses
is.null(mod) and falls back on presences + background
(data_2) (#478)tune_svm: removed na.omit() on partition
performance so all combinations are compared across the same partitions
(#478)fit_gbm: final model now uses
bag.fraction = 0.9, consistent with the CV models;
tune_gbm validity guard also uses 0.9 (#478)occfilt_geo: moran method no longer collapses to one
record (Inf distance) when no Moran’s I falls in the
selection window; the closest value is used, and the threshold
comparison is now numeric (#479)sample_pseudoabs: factor maskval now
matches raster category IDs in the kmeans, env_const, env_clust and
env_kmeans methods (#479)get_absences: NA in the species column no longer
creates phantom NA rows (#479)calib_area: mcp/bmcp with three or more groups no
longer errors (#479)part_sband: fixed uninitialised
imoran_bands_c assignment (#479)\donttest{}); examples of sdm_directory() and
homogenize_na() / extra_eval() no longer leave
files in the working directory or alter par()sdm_directory: main_dir no longer defaults
to a sub-folder of the working directory and must be provided by the
userset.seed() with a fixed number
(fit_*, occfilt_geo,
correct_colinvar, p_extra, …). Results can be
reproduced by calling set.seed() before running them;
tune_*, sdm_varimp and
sdm_uncertainty draw the seeds used by parallel workers
from the caller’s random-number streamtune_gbm, tune_max, tune_net,
tune_raf, tune_svm, sdm_varimp
and sdm_uncertainty: no cluster is started when
n_cores = 1, the computation runs sequentially in the
current sessionoccfilt_geo: the warn option is restored
when the function exitsfit_gau() and
esm_gau(), fit_max() and
tune_max(): print() and cat()
calls replaced by message() or warning() (or
removed)\donttest{}), test files that take more than a
fraction of a second are skipped as a whole on CRAN with
skip_on_cran() (they run when NOT_CRAN=true),
and vignette v04 is now precomputed (source in
vignettes/v04_Red_fir_example.Rmd.orig)DESCRIPTION: reworded “ensembling” in the Description
fieldget_absences: Function to build a presence-absence
database from multi-species occurrence data, by @sjevelazcoocc_reproject: Function to reproject occurrence
coordinates to a new coordinate reference system, by @sjevelazcocorrect_colinvar: Unicode box-drawing characters in the
documentation’s example directory tree were replaced with plain ASCII,
fixing a LaTeX error that caused the PDF manual build to fail on CRAN,
by @sjevelazcov07_Complete_workflow: two dataset links were
missing the https:// scheme (www.doi.org/...),
which CRAN flagged as invalid URIs; fixed to
https://doi.org/..., by @sjevelazcotune_max: fixed a bug where the default hyperparameter
grid (used when grid = NULL) tested a regmult
step of 0.05 instead of the documented 0.5, producing ~10x more
combinations than intended, by @sjevelazcoskip_on_cran(), by @sjevelazcotune_* and
sdm_uncertainty test files behind
skip_on_cran() (kept a representative case running on CRAN
for each), and by shrinking dataset sizes and search-grid sizes
(part_sband, part_sblock,
occfilt_env, occfilt_geo) further in the
vignettes, by @sjevelazcotest_that() block per test file (or none for
the slowest files) now run only when NOT_CRAN=true, and
vignettes v01, v02, v03,
v05 and v06 are now precomputed (source in
vignettes/*.Rmd.orig, excluded from the build), by @sjevelazco\donttest{} examples of the slowest
functions (sdm_varimp, fit_max,
fit_gau, tune_*, esm_gam,
fit_gam, occfilt_geo,
sdm_summarize, esm_max) by using smaller
hyperparameter grids, fewer folds/replicates and subsampled datasets
(~10 min -> ~3.5 min locally), by @sjevelazcofit_raf: the ntree argument was ignored in
the no-partition fit and in the cross-validation fits (hardcoded to
500); it is now used everywhere, reported in #474, by
@sjevelazcotune_raf: the final model now uses the tuned
ntree instead of the randomForest default, #474, by
@sjevelazcoesm_gbm: n_trees and
shrinkage were hardcoded and are now forwarded to
fit_gbm; esm_net: size and
decay are now forwarded to fit_net, #474, by
@sjevelazcoesm_svm: performance_part was labelled
esm_gau; now esm_svm. fit_svm and
fit_max: partition ids in performance_part
were wrong (earlier folds were relabelled), #474, by
@sjevelazcoREADME.md, whose URL
returned HTTP 429 in CRAN’s URL check, by @sjevelazcosdm_eval: fixed a bug in the KAPPA metric calculation,
by @sjevelazcosample_background and sample_pseudoabs:
fixed three bugs in categorical maskval/k-means handling
reported in #472:
maskval on a factor raster was resolved to its row position
in the levels table instead of its actual category ID; the k-means
branch of sample_pseudoabs masked and overwrote the
environmental raster with the region raster instead of the other way
around; and the internal kf() helper could fail with
“differing number of rows” when the environmental raster had no NA
cells, by @sjevelazcomap_env_dist: C++ function to calculate Euclidean and
Mahalanobis distance was implemented, by @sjevelazco 463extra_eval: C++ function to calculate Euclidean and
Mahalanobis distance was implemented, by @sjevelazco 463fit_dom: C++ function to calculate gower distance was
optimized, by @sjevelazco 463map_env_dist: C++ function to calculate gower distance
was optimized, by @sjevelazco 463msdm_posteriori: a new argument that assumes unsuitable
cells with presences as patches. This is only used in the ‘lq’ approach,
by @sjevelazco 461sdm_eval: Boyce metric was readapted based on enmSdmX
package, by @sjevelazco 457msdm_posteriori: now car return semi-binary suitability
maps, by @sjevelazco 455part_senv: new argument to use or not
presences-absences coordinate to perform environmental partition, by
@sjevelazco 445map_env_dist was improved. Now it is possible to
calculate Euclidean and Mahalanobis distances in addition to the Gower
distance (Domain algorithm), by @sjevelazco.sdm_eval() now calculates three additional performance
metrics: the Matthews Correlation Coefficient (MCC). the Continuous
Ranked Probability Score (CRPS), and a prevalence-weighted average of
TPR and TNR (W_TPR_TNR), by @sjevelazco 445.fit_gbm() and tune_gbm() were improved to
prevent the argument is of length zero error. This was
achieved by adding a safeguard that dynamically adjusts
n.minobsinnode based on the size of the training data
partition, ensuring it remains valid for the gbm algorithm,
by @sjevelazco 445.sdm_uncertainty This new function calculates species
distribution model uncertainty using a bootstrap procedure, by @sjevelazco #442fit_,
tune_,
esm_
and fit_ensemble functions now return a performance table
for each partition and replicate (performance_part), by @sjevelazco #432 and 434p_pdp was improved to depict exactly training range
values when projection data are used, by @sjevelazco #429sample_pseudoabs a sample approach was implemented
based on environmental K-means, by @sjevelazco #410sample_pseudoabs K-means step was improved for the
geoenv_const_kmeans approach, by @sjevelazco #410p_extra Now can handle binary maps to plot suitability
or extrapolation values in the geographical or environmental space, by
@sjevelazco 418map_env_dist This new function calculates environmental
distance between presences and projection data. Only the nearest Gower
distance was implemented (Domain algorithm), by @sjevelazco 419fit_dom This is a new function to fit and validate the
Domain algorithm, by @sjevelazcooccfilt_geo adapted to test different values for the
three methods by @sjevelazco in 386occfilt_env now can filter with several bins by @sjevelazco in 388occfilt_select was created and a test protocol was
written by @sjevelazco in 389occfit_select included in vignette and website by @sjevelazco in 390sdm_predict to handle 0 weight value for
weighted mean by @sjevelazco in 391occfilt_select was improved by @sjevelazco in 392correct_colinvar can be used with species points by
@sjevelazco in 394sdm_varimp was added and tested by @mrose048 and @sjevelazco in 399calib_area by @sjevelazco in 400interp function by @sjevelazco in 362occfilt_geo by @sjevelazco in 364occfilt_geo by @sjevelazco in 370occfilt_geo by @sjevelazco in 371esm_ functions were improved by @sjevelazco in 373 & 373correct_colinvar by @sjevelazco in 374correct_colinvar by @sjevelazco in 375calib_area was sped up by @sjevelazco in 377correct_colivar was fixed and improved, FA method by
@sjevelazco in 383occfilt_geo ‘defined’ method was fixed by @sjevelazco in 384correct_clinvar()occfilt_geo has a new argument “rep” to control number
o repetition to filter occurrencesextra_eval.part_sblock and part_sbandcorrect_colinvar to speed up function
when using maxcell argumentcorrect_colinvar to project PCA for
other time periodscorrect_colinvar It is now possible to
sample rasters to reduce machine memory and speed up the processsdm_predict It is possible predict
model in chunks to reduce machine memoryp_extra, p_pdp, and p_bpdp
were fixedp_bpdp Bivariate Partial Dependent
Plotdata_bpdp Calculate data to construct
bivariate partial dependence plotsp_dpd Calculate data to construct
partial dependence plotsp_extra Graphical exploration of
extrapolation or suitability pattern in the environmental and
geographical spacep_pdp Partial Dependent Plotdata_pdp Calculate data to construct
partial dependence plotsmsdm_posteriorisample_background and
sample_pseudoabsmsdm_posterioriextra_eval can use tibble or SpatRaster object in
env_calib argumentextra_truncate has a new argument to define values used
for model truncation and documentation was improved. #These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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