TAG: Transformed Additive Gaussian Processes

Implement the transformed additive Gaussian (TAG) process and the transformed approximately additive Gaussian (TAAG) process proposed in Lin and Joseph (2020) <doi:10.1080/00401706.2019.1665592>. These functions can be used to model deterministic computer experiments, obtain predictions at new inputs, and quantify the uncertainty of the predictions. This research is supported by a U.S. National Science Foundation grant DMS-1712642 and a U.S. Army Research Office grant W911NF-17-1-0007.

Version:	0.5.1
Depends:	R (≥ 3.5.0)
Imports:	Rcpp, DiceKriging, Matrix, mgcv, FastGP, mlegp, randtoolbox, foreach
LinkingTo:	Rcpp, RcppArmadillo
Published:	2021-06-07
Author:	Li-Hsiang Lin and V. Roshan Joseph
Maintainer:	Li-Hsiang Lin <llin79 at gatech.edu>
License:	GPL-2
NeedsCompilation:	yes
CRAN checks:	TAG results

Documentation:

Reference manual:

TAG.pdf

Downloads:

Package source:	TAG_0.5.1.tar.gz
Windows binaries:	r-devel: TAG_0.5.1.zip, r-release: TAG_0.5.1.zip, r-oldrel: TAG_0.5.1.zip
macOS binaries:	r-release (arm64): TAG_0.5.1.tgz, r-oldrel (arm64): TAG_0.5.1.tgz, r-release (x86_64): TAG_0.5.1.tgz, r-oldrel (x86_64): TAG_0.5.1.tgz
Old sources:	TAG archive

Linking:

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