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GPpenalty: Penalized Likelihood in Gaussian Processes

Implements maximum likelihood estimation for Gaussian processes, supporting both isotropic and separable models with predictive capabilities. Includes penalized likelihood estimation following Li and Sudjianto (2005, <doi:10.1198/004017004000000671>), using decorrelated prediction error (DPE)-based metrics, motivated by Mahalanobis distance, that account for uncertainty. Includes cross validation techniques for tuning parameter selection. Designed specifically for small datasets.

Version: 1.0.0
Depends: R (≥ 3.5.0)
Imports: Rcpp, doParallel, foreach
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat (≥ 3.0.0)
Published: 2025-11-15
DOI: 10.32614/CRAN.package.GPpenalty
Author: Ayumi Mutoh [aut, cre]
Maintainer: Ayumi Mutoh <amutoh at ncsu.edu>
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: README, NEWS
CRAN checks: GPpenalty results

Documentation:

Reference manual: GPpenalty.html , GPpenalty.pdf

Downloads:

Package source: GPpenalty_1.0.0.tar.gz
Windows binaries: r-devel: GPpenalty_1.0.0.zip, r-release: GPpenalty_0.1.0.zip, r-oldrel: GPpenalty_0.1.0.zip
macOS binaries: r-release (arm64): GPpenalty_0.1.0.tgz, r-oldrel (arm64): GPpenalty_0.1.0.tgz, r-release (x86_64): GPpenalty_1.0.0.tgz, r-oldrel (x86_64): GPpenalty_1.0.0.tgz
Old sources: GPpenalty archive

Linking:

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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.
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