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scp_geostatistical(): 1D coordinate vectors passed as
s0 are now properly coerced to a 1-row coordinate matrix
matching training dimensions.plot.spconform_diagnose(): replaced uneven matrix dimension
recycling with an explicit layout matrix when n_panels = 3,
preventing duplicate panel drawing.scp_geostatistical()
and scp_areal() guarding against missing (NA)
values in coordinates and responses.na.rm support in
coverage_report() to prevent silent vector recycling
errors.scp_geostatistical(): bandwidth is now evaluated once prior
to the query loop rather than recomputed per target location, yielding
up to a 50x speedup on large query grids.k_neighbors argument to
scp_geostatistical() for localized \(k\)-NN calibration, defaulting to
NULL for complete backward compatibility.coverage_report() now evaluates and reports the
strictly proper Winkler Interval Score (winkler_score)
alongside empirical coverage and mean interval width.spconform objects:
predict.spconform() to extract point predictions
or prediction intervals (fit, lwr,
upr).residuals.spconform() to compute raw response
residuals or absolute calibration scores.as.data.frame.spconform() to coerce conformal
objects into tidy data frames.diagnose() now returns a classed
spconform_diagnose object with dedicated
print.spconform_diagnose() and
plot.spconform_diagnose() methods.R/scp_areal.R for full ASCII compliance.spconform on CRAN.scp_geostatistical()).scp_areal()).spatial_kernel_weights(),
areal_neighbor_weights()).diagnose()).print(),
summary(), plot(),
coverage_report()).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.
Health stats visible at Monitor.