plgp: Particle Learning of Gaussian Processes

Sequential Monte Carlo (SMC) inference for fully Bayesian Gaussian process (GP) regression and classification models by particle learning (PL) following Gramacy & Polson (2011) <doi:10.48550/arXiv.0909.5262>. The sequential nature of inference and the active learning (AL) hooks provided facilitate thrifty sequential design (by entropy) and optimization (by improvement) for classification and regression models, respectively. This package essentially provides a generic PL interface, and functions (arguments to the interface) which implement the GP models and AL heuristics. Functions for a special, linked, regression/classification GP model and an integrated expected conditional improvement (IECI) statistic provide for optimization in the presence of unknown constraints. Separable and isotropic Gaussian, and single-index correlation functions are supported. See the examples section of ?plgp and demo(package="plgp") for an index of demos.

Version: 1.1-12
Depends: R (≥ 2.4), mvtnorm, tgp
Suggests: ellipse, splancs, interp
Published: 2022-10-19
DOI: 10.32614/CRAN.package.plgp
Author: Robert B. Gramacy
Maintainer: Robert B. Gramacy <rbg at>
License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL]
NeedsCompilation: yes
Materials: ChangeLog
In views: ExperimentalDesign
CRAN checks: plgp results


Reference manual: plgp.pdf


Package source: plgp_1.1-12.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): plgp_1.1-12.tgz, r-oldrel (arm64): plgp_1.1-12.tgz, r-release (x86_64): plgp_1.1-12.tgz, r-oldrel (x86_64): plgp_1.1-12.tgz
Old sources: plgp archive

Reverse dependencies:

Reverse imports: AHM, maximin, RNAmf
Reverse suggests: dynaTree, reglogit


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