Package: GPM 3.0.1

Ramin Bostanabad

GPM: Gaussian Process Modeling of Multi-Response and Possibly Noisy Datasets

Provides a general and efficient tool for fitting a response surface to a dataset via Gaussian processes. The dataset can have multiple responses and be noisy (with stationary variance). The fitted GP model can predict the gradient as well. The package is based on the work of Bostanabad, R., Kearney, T., Tao, S. Y., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. International Journal for Numerical Methods in Engineering, 114, 501-516.

Authors:Ramin Bostanabad, Tucker Kearney, Siyo Tao, Daniel Apley, and Wei Chen

GPM_3.0.1.tar.gz
GPM_3.0.1.tar.gz(r-4.5-noble)GPM_3.0.1.tar.gz(r-4.4-noble)
GPM_3.0.1.tgz(r-4.4-emscripten)GPM_3.0.1.tgz(r-4.3-emscripten)
GPM.pdf |GPM.html
GPM/json (API)

# Install 'GPM' in R:
install.packages('GPM', 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
  • 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.

2.86 score 73 scripts 184 downloads 71 mentions 11 exports 11 dependencies

Last updated 6 years agofrom:0d1b5a1f18. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-linux-x86_64NOTENov 05 2024

Exports:AuxilCorrMat_SymCorrMat_VecCppSolveDrawEigenFitLowerCholNLogLNLogL_GPredict

Dependencies:codetoolsdoParallelforeachiteratorslatticelhspracmarandtoolboxRcppRcppArmadillorngWELL