gplite: Implementation for the Most Common Gaussian Process Models

Implements the most common Gaussian process (GP) models using Laplace and expectation propagation (EP) approximations, maximum marginal likelihood (or posterior) inference for the hyperparameters, and sparse approximations for larger datasets.

Version: 0.12.0
Depends: R (≥ 3.4.0)
Imports: Matrix, methods, Rcpp
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, knitr, rmarkdown, ggplot2
Published: 2021-04-30
Author: Juho Piironen [cre, aut]
Maintainer: Juho Piironen <juho.t.piironen at gmail.com>
License: GPL-3
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: gplite results

Documentation:

Reference manual: gplite.pdf
Vignettes: gplite Quickstart

Downloads:

Package source: gplite_0.12.0.tar.gz
Windows binaries: r-devel: gplite_0.12.0.zip, r-devel-UCRT: gplite_0.12.0.zip, r-release: gplite_0.12.0.zip, r-oldrel: gplite_0.12.0.zip
macOS binaries: r-release (arm64): gplite_0.12.0.tgz, r-release (x86_64): gplite_0.12.0.tgz, r-oldrel: gplite_0.12.0.tgz
Old sources: gplite archive

Linking:

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