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libgomp is no longer linked on macOS, where libKriging
is built without OpenMP and libgomp only comes with
gfortran (“library ‘gomp’ not found”);R_HOME to find
the Fortran compiler: under R CMD check, a bare
R refuses to run, so libKriging was silently not built
(“‘libKriging/utils/lkalloc.hpp’ file not found”).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.
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.