Metric functions now return NA in all cases where they previously returned NaN.
This improves cross-platform consistency; in particular, MacOS often returned
NA when every other platform would return NaN. (#63)
ww_multi_scale() now handles classification and class probability metrics better
when called with raster arguments (either to data or to truth and estimate):
ww_multi_scale() will now convert
truth and estimate to factors before passing them to the metric set.
Thanks to @nowosad for the report in #60 (#61).ww_multi_scale() will convert
truth to a factor and will pass estimate as an unnamed argument. (#62)ww_multi_scale()
will error. (#62)ww_multi_scale() now warns if you provide crs as an argument to sf::st_make_grid() via .... Grids created by this function will always take their CRS from data.
ww_multi_scale() now throws an error if you pass arguments via ... while also providing a list of grids (because those arguments would be ignored).
ww_multi_scale() is now faster when data is an sf object, particularly when grids are created by passing arguments to sf::st_make_grid() (rather than passing grids via grids).
ww_multi_scale() did not correctly handle grid cellsizes with units. Units (set using the units package) are now respected.
ww_multi_scale() using sf data always returned "1" for truth and estimate counts. This was because counts were calculated post-aggregation by mistake. Calculation order has been fixed, and these counts should now be correct.
ww_build_neighbors() (and by extent, every spatial dependence metric) will
no longer calculate neighbors for non-point or non-polygon geometries.
Functions dealing with local Moran's I and related p-values (both data frame and vector variants) now return unnamed vectors.
Added a new method to support passing a SpatRaster to data in
ww_multi_scale(), with truth and estimate being indices used to subset
data. This is a bit faster than passing SpatRaster objects to truth and
estimate, as extraction is only done once per grid rather than twice, but
does not easily support passing R functions to aggregation_function.
Metric functions now have better error messages including the name of the function the user called that errored, not the internal function that errored. Huge thanks to @EmilHvitfeldt for their PR (#40).
Data frame metric functions now guarantee that .estimate will be an unnamed
vector.
The sf method for ww_multi_scale() is now much faster (and more memory
efficient).
Fixed the warning when ww_area_of_applicability() calculates an AOA
threshold of 0. It now includes "Did you accidentally pass the same data as
testing and training?" as a bullet.
Removed combination functions -- ww_global_geary, ww_global_moran,
ww_local_geary, ww_local_moran, ww_local_getis_ord. Use metric_set()
to combine functions instead.
Renamed ww_local_getis_ord_pvalue_vec() and variants to
ww_local_getis_ord_g_pvalue_vec(); this change allows internal functions to
work properly, and makes it easier for the output to indicate if the p-value
is associated with a g or g* value.
Yardstick metrics will no longer include geometry columns in their returns.
The na_action argument to ww_area_of_applicability() has been replaced by
na_rm, with a default value of FALSE.
na_rm is now TRUE by default for non-spatial-autocorrelation functions.
NA values will cause spatial-autocorrelation functions to fail with an error.
Added functions (primarily ww_multi_scale()) and a vignette for multi-scale
assessment of model predictions.
Added functions to calculate metrics from Ji and Gallo (2006) and Willmott
(1981, 1982, 2012): ww_agreement_coefficient(),
ww_systematic_agreement_coefficient(),
ww_unsystematic_agreement_coefficient(), ww_unsystematic_mpd(),,
ww_systematic_mpd(), ww_unsystematic_rmpd(), ww_systematic_rmpd(),
ww_willmott_d(), ww_willmott_dr(), ww_willmott_d1(),
ww_systematic_mse(), ww_unsystematic_mse(), ww_systematic_rmse(),
ww_unsystematic_rmse(). Note that ww_willmott_dr() uses the version
from Willmott (2012); other implementations (sometimes called "d1r") seem to
use an unbounded variant that I haven't found a reference to support.
Changed a call in ww_area_of_applicability() to use FNN for nearest
neighbors, rather than fields. This sped up prediction by a lot.
Rewrote README and moved the old content to a new vignette on assessing the spatial dependency in model residuals.
Added a dependency on FNN.
Minimum version for dplyr has been bumped to 1.1.0
Added functions for automatically constructing nb and listw objects
Added functions for global and local Geary's C values.
Added functions for local Getis-Ord G and G* values.
Added functions for global and local Moran's I values.
Added a NEWS.md file to track changes to the package.