NEWS.md
In spCF: Coarse-to-Fine Spatial and Spatio-Temporal Modeling

spCF 0.2.2

negbin(): negative binomial family with estimated (or fixed) dispersion for cf_glm_hv(), cf_glm(), cf_dglm_hv() and cf_dglm(); theta is re-estimated after the initial GLM and after each accepted scale. poisson(link = "identity") is also supported.
se_type = "prediction" now returns a moment-matched observation predictive, calibrated to 95% holdout coverage, for the Gamma, inverse Gaussian, quasipoisson and quasibinomial families.
predict() methods for cf_lm(), cf_glm() and cf_dglm() fits: prediction at new sites (and, for cf_dglm(), new time points) without the training data. The fits keep the local estimates at the knots of every selected scale (well under 1 MB in typical fits), so the cost of a prediction does not depend on the size of the training data (about 0.25 s for 22,500 sites, for 3,000 or 100,000 training sites alike). The result is identical to fitting with the same sites as coords0. predict() returns the predictive mean, SD and quantiles at any levels (probs); without new sites it gives these at the sample sites.
cf_lm(), cf_glm() and cf_dglm() gain keep_scales (default TRUE). With keep_scales = FALSE the scale-wise processes Z, Z_sd, Z0 and Z0_sd are not kept, which makes the fitted object several times smaller; predictions are unchanged, and only sp_scalewise() needs them.

Time points in time0 that are not training time points no longer enter the AR(1) time grid of the fit. They are predicted from the smoothed per-knot states: bridged between the neighbouring training time points (the AR(1) step split in proportion to the time differences), or forecast / backcast by the time difference over the median spacing of the training time points. The fit, beta_tv and sd_summary therefore no longer depend on time0 (an interior time point used to add a step to the AR(1) grid and so changed the fit), and predict() reproduces the predictions later. Forecasts one spacing ahead are unchanged.

The quantile tables pred_q, pred0_q, pred_q_signal and pred0_q_signal (15 columns each) are no longer stored. mod$pred_q and the other fields still return them at the same 15 levels, computed on access and identical to the stored tables of earlier versions.
The prediction tables pred and pred0 had character row names inherited from named prediction vectors, which made them about five times larger than their numbers; they now carry automatic row names.
Together, a cf_dglm() fit of 2,000 sites x 100 time points shrinks from 155 MB to 108 MB (41 MB with keep_scales = FALSE), of which 24 MB are the knot states kept for predict(), and a cf_lm() fit of 100,000 sites from 90 MB to 69 MB (11 MB).

cf_lm() and cf_glm(): the calibrated variance of each spatial scale is now bounded by that scale's share of the field variance, so the predictive SD grows smoothly with the distance to the data instead of drawing rings around isolated sites (see Details in ?cf_lm). Point predictions and coefficients are unchanged.
cf_dglm(): a distance-aware field variance of the mean, an information-scaled calibration, and a floor on the field variance (see Details in ?cf_dglm). Coverage of 95% mean intervals at held-out sites is close to nominal in simulations (it was 0.58-0.75). Point predictions are unchanged.
The argument sill_cap of cf_dglm() is removed: the field variance is always capped at the marginal variance of the fitted field (as with the default sill_cap = TRUE before).
The noise part of the cf_lm() coefficient standard errors (se_method = "opt") is rescaled to a nearest-neighbour nugget estimate.

spCFmap(): irregular sites with coordinates on a fine common resolution (e.g. integer metres, as the meuse sample sites) were taken for a lattice and drawn as invisible one-metre pixels; they are now filled from the nearest site.
spCFmap(): irregular prediction sites are still drawn as a raster filled from the nearest site, but only within a circle of a common radius around each site, so nothing far from a prediction site is coloured. The default radius is 0.75 times the median distance to the nearest site (at least 1/300 of the diagonal of the region), and a "Circle size" slider scales it. Regular lattices are drawn as before. For a cf_dglm() fit with prediction sites, the time slider spans the prediction time points (min(time0) to max(time0)) instead of the whole training period, and steps by their spacing when they are equally spaced.
cf_dglm(): validation_MAE in e_summary was the absolute mean error (absolute bias) instead of the mean absolute error.
cf_downscale(): the intercept row of beta was labelled x or V1; it is now Intercept, and unnamed covariates are labelled x1, x2, ...
cf_lm(): no longer warns when the holdout predictions are constant; validation_R2 is then NA.
The calibration of the per-scale variance no longer fails when the holdout moment equation has no root in its search interval.