Skip to content

NEWS.md

Latest commit

 

History

History
93 lines (71 loc) · 4.08 KB

NEWS.md

File metadata and controls

93 lines (71 loc) · 4.08 KB
Raw

rlibkriging 1.2-3

Fixes

  • Fix installation on macOS with the flang Fortran compiler (R-devel):
    • libgomp is no longer linked on macOS, where libKriging is built without OpenMP and libgomp only comes with gfortran ("library 'gomp' not found");
    • the libKriging build now calls R through 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").

rlibkriging 1.2-2

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).

Breaking changes

  • The Vecchia objective introduced in 1.1-0 is renamed: objective = "VLL" / "VLL(m)" becomes "LLVecchia" / "LLVecchia(m)". No alias is kept, so "VLL(m)" now raises Unsupported fit objective. Results are unchanged.

New features

  • New 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.
  • New 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.

Fixes

  • 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.
  • Faster 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.
  • Packaging: NAMESPACE no longer depends on roxygen2 succeeding at build time, and hidden files of the bundled libKriging sources are no longer shipped.

rlibkriging 1.1-1

Fixes

  • Shrink 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).

rlibkriging 1.1-0

New features

  • 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).

Changes

  • 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.

Fixes

  • Fix a possible deadlock when forking after threads were created.
  • Numerous build and portability fixes (Windows, macOS deployment target).