Package: TAG 0.5.1

Li-Hsiang Lin

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

TAG_0.5.1.tar.gz
TAG_0.5.1.tar.gz(r-4.5-noble)TAG_0.5.1.tar.gz(r-4.4-noble)
TAG_0.5.1.tgz(r-4.4-emscripten)TAG_0.5.1.tgz(r-4.3-emscripten)
TAG.pdf |TAG.html✨
TAG/json (API)

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

Peer review:

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

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

openblascpp

3.02 score 105 scripts 152 downloads 66 mentions 9 exports 18 dependencies

Last updated 4 years agofrom:27fb769e41. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 30 2024
R-4.5-linux-x86_64NOTEDec 30 2024

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

Dependencies:codetoolsDiceKrigingFastGPforeachiteratorslatticeMASSMatrixmgcvmlegpmvtnormnlmerandtoolboxrbenchmarkRcppRcppArmadilloRcppEigenrngWELL