Based on libKriging 1.2.2. Supersedes 1.2-1, which failed to install
on CRAN (the submitted NAMESPACE lacked
importFrom(DiceKriging, km) and the KM /
as.km exports).
objective = "VLL" / "VLL(m)" becomes
"LLVecchia" / "LLVecchia(m)". No alias is
kept, so "VLL(m)" now raises
Unsupported fit objective. Results are unchanged.LLNystrom(k) objective: a fixed-landmark Nystrom
low-rank approximation of the covariance for large designs, costing O(n
k^2) per evaluation. The $nystrom_rank() accessor gives the
rank of such a fit.subsetOfData(): k-means (or random) pre-fit row
subsetting for large designs (indices are 1-based).WarpKriging now has the same accessors as
Kriging: noise(), warp_params(),
optim(), objective() and
covMat(X1, X2). It also accepts numeric
parameters seeds with optim = "none" (to
rebuild a model with frozen hyper-parameters), and
update(..., noise_u =) /
update_simulate(..., noise_u =). noise = and
parameters = can now be used together.predict(..., return_deriv = TRUE) returned derivatives
off by a factor scaleX when the model was fitted with
normalize = TRUE.WarpKriging: the analytical warp-parameter gradient was
wrong for every continuous warp, so the optimizer never found a
non-trivial warp. Warpings that assume inputs in [0, 1]
(knots, kumaraswamy, boxcox,
neural_mono, mlp, mlp_joint) now
rescale inputs from their training range; fits on inputs spanning
exactly [0, 1] are unchanged.optim = "none" with a light Vecchia fit ignored the
LLVecchia(m) objective.fit(), predict() and above all
update(refit = FALSE): the inverse covariance matrix is now
computed only when a gradient needs it.simulate.WarpKriging no longer self-qualifies with
:::, WarpKriging is registered with
setOldClass (no load-time warning), and the
save / load examples remove their temporary
file.NAMESPACE no longer depends on
roxygen2 succeeding at build time, and hidden files of the
bundled libKriging sources are no longer shipped.test-NestedKriging.R design/test sizes to avoid
a check timeout on slow CRAN workers
(e.g. r-devel-linux-x86_64-fedora-*, which exceeded the
45-minute test time limit under 1.1-0).New NestedKriging class: a divide-and-conquer
Gaussian process for large designs. The data are partitioned into
groups, one Kriging submodel is fitted per group with a
common prior, and predictions are aggregated with the optimal
nested-kriging aggregation ("NK", interpolating) or a
product-of-experts rule ("PoE", "gPoE",
"BCM", "rBCM").
New Vecchia approximated log-likelihood objective for large
designs: fit a Kriging model with
objective = "VLL(m)" (or "VLL", default
m = 30), costing O(n m^3) per evaluation instead of
O(n^3).
Kriging() / fit():
objective now also accepts "VLL" /
"VLL(m)", and regmodel now accepts
"quadratic".
Kriging() / fit(): the
noise argument has been moved to the last
position, for consistency with WarpKriging and the other
language bindings. Code that passes noise by name is
unaffected; positional calls that relied on noise being the
4th argument must be updated.