Package: DynamicGP 1.1-9

Ru Zhang

DynamicGP: Modelling and Analysis of Dynamic Computer Experiments

Emulating and solving inverse problems for dynamic computer experiments. It contains two major functionalities: (1) localized GP model for large-scale dynamic computer experiments using the algorithm proposed by Zhang et al. (2018) <arxiv:1611.09488>; (2) solving inverse problems in dynamic computer experiments. The current version only supports 64-bit version of R.

Authors:Ru Zhang [aut, cre], Chunfang Devon Lin [aut], Pritam Ranjan [aut], Robert B Gramacy [ctb], Nicolas Devillard [ctb], Jorge Nocedal [ctb], Jose Luis Morales [ctb], Ciyou Zhu [ctb], Richard Byrd [ctb], Peihuang Lu-Chen [ctb], Berend Hasselman [ctb], Jack Dongarra [ctb], Jeremy Du Croz [ctb], Sven Hammarling [ctb], Richard Hanson [ctb], University of Chicago [cph], University of California [cph]

DynamicGP_1.1-9.tar.gz
DynamicGP_1.1-9.tar.gz(r-4.5-noble)DynamicGP_1.1-9.tar.gz(r-4.4-noble)
DynamicGP_1.1-9.tgz(r-4.4-emscripten)DynamicGP_1.1-9.tgz(r-4.3-emscripten)
DynamicGP.pdf |DynamicGP.html✨
DynamicGP/json (API)

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

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • fortran– Runtime library for GNU Fortran applications
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

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

fortranopenblascppopenmp

1.00 score 6 scripts 321 downloads 6 exports 2 dependencies

Last updated 2 years agofrom:1eb19d2fd1. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 11 2024
R-4.5-linux-x86_64OKOct 12 2024

Exports:ESL2DknnsvdGPlasvdGPsaEISL2DsvdGP

Dependencies:lhsRcpp