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>), with cross-validation guided by decorrelated prediction error (DPE) metric. DPE metric, motivated by Mahalanobis distance, serves as evaluation criteria that accounts for predictive uncertainty in tuning parameter selection (Mutoh, Booth, and Stallrich, 2025, <doi:10.48550/arXiv.2511.18111>). Designed specifically for small datasets.

Version:	1.0.1
Depends:	R (≥ 3.5.0)
Imports:	Rcpp, doParallel, foreach
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	testthat (≥ 3.0.0)
Published:	2025-11-26
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_1.0.1.tar.gz
Windows binaries:	r-devel: GPpenalty_1.0.0.zip, r-release: GPpenalty_0.1.0.zip, r-oldrel: GPpenalty_1.0.0.zip
macOS binaries:	r-release (arm64): GPpenalty_1.0.1.tgz, r-oldrel (arm64): GPpenalty_1.0.1.tgz, r-release (x86_64): GPpenalty_1.0.1.tgz, r-oldrel (x86_64): GPpenalty_1.0.1.tgz
Old sources:	GPpenalty archive

Linking:

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