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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 |
| DOI: | 10.32614/CRAN.package.TAG |
| 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 |
| Reference manual: | TAG.pdf |
| 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 |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.