bean 0.2.0
Major changes
find_env_resolution() no longer uses a geometric
“elbow” rule on nearest- neighbour distances. It now selects a
kernel-density bandwidth for each environmental variable using the
Sheather-Jones plug-in estimator (default), with Silverman and Scott
rules also available via the new method argument. The
bandwidth is a statistically defensible choice for the edge length of an
environmental grid cell, and the new implementation is faster and more
robust to ties.
rgl is now a Suggests dependency rather than
an Imports. 3-D plots still work when rgl is
installed; otherwise plot.bean_ellipsoid() falls back to a
2-D view of the first two requested dimensions.
- The S3 method
predict.bean_ellipsoid() is now
documented under its canonical name (previously the help page was
generated as predict.ellipsoid_bean).
- Vignettes have been reorganised:
bean-overview — quickstart introduction.
data-preparation — preparing rasters and
occurrences.
environmental-thinning — resolution selection and
thinning.
niche-modeling — fitting ellipsoids and projecting
suitability. All vignettes now build without requiring
rgl.
CRAN readiness
DESCRIPTION: trimmed Imports to
MASS, stats, terra; moved
rgl, ggplot2 and dplyr to
Suggests (with dplyr removed entirely from the
package — it was only used in one vignette).
- Added a
tests/testthat/ test suite covering
find_env_resolution(), thin_env_nd(),
thin_env_center(), fit_ellipsoid() and
predict.bean_ellipsoid().
- Datasets are now documented as user-visible (not
\keyword{internal}).
- Replaced
\dontrun{} examples that depended on missing
files with \donttest{} examples that use the shipped sample
data.
prepare_bean() now uses match.arg() for
transform.
- Cleaned up
R/globals.R (was carrying many unused
identifiers).
Minor
print.bean_ellipsoid() output reformatted for
clarity.
- Internal helper
.bean_ellipse_polygon() consolidates
the 2-D ellipse polygon code path used by both
fit_ellipsoid() and
plot.bean_ellipsoid().
bean 0.1.2
- Earlier development versions; see commit history on GitHub.