Package: TAG 0.5.1

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.

Authors:Li-Hsiang Lin and V. Roshan Joseph

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TAG.pdf |TAG.html
TAG/json (API)

# Install 'TAG' in R:
install.packages('TAG', repos = c('https://llin716.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.02 score 104 scripts 186 downloads 66 mentions 9 exports 18 dependencies

Last updated 3 years agofrom:27fb769e41. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 13 2024
R-4.5-win-x86_64NOTENov 13 2024
R-4.5-linux-x86_64NOTENov 13 2024
R-4.4-win-x86_64NOTENov 13 2024
R-4.4-mac-x86_64NOTENov 13 2024
R-4.4-mac-aarch64NOTENov 13 2024
R-4.3-win-x86_64NOTENov 13 2024
R-4.3-mac-x86_64NOTENov 13 2024
R-4.3-mac-aarch64NOTENov 13 2024

Exports:check.TAAGcv.TAAGinitial.TAGplotTAGpred.TAAGpred.TAGTAAGTAGTAG1step

Dependencies:codetoolsDiceKrigingFastGPforeachiteratorslatticeMASSMatrixmgcvmlegpmvtnormnlmerandtoolboxrbenchmarkRcppRcppArmadilloRcppEigenrngWELL