GauPro: Gaussian Process Fitting

Fits a Gaussian process model to data. Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an 'R6' object and can be easily updated with new data. There are options to run in parallel (not for Windows), and 'Rcpp' has been used to speed up calculations. Other R packages that perform similar calculations include 'laGP', 'DiceKriging', 'GPfit', and 'mlegp'.

Version: 0.2.2
Imports: Rcpp, R6, lbfgs
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, knitr, rmarkdown, microbenchmark, numDeriv, MASS
Published: 2017-09-11
Author: Collin Erickson
Maintainer: Collin Erickson <collinberickson at>
License: GPL-3
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: GauPro results


Reference manual: GauPro.pdf
Vignettes: Leave-one-out cross-validation and error correction
A Guide to the GauPro R package
Introduction to Gaussian Processes
Derivatives for estimating Gaussian process parameters
Spatial derivatives of Gaussian process models
Package source: GauPro_0.2.2.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: GauPro_0.2.2.tgz, r-oldrel: GauPro_0.2.2.tgz
Old sources: GauPro archive

Reverse dependencies:

Reverse imports: ParBayesianOptimization
Reverse suggests: IGP


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