Package: laGP 1.5-9

Robert B. Gramacy

laGP: Local Approximate Gaussian Process Regression

Performs approximate GP regression for large computer experiments and spatial datasets. The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine. OpenMP and GPU features may require special compilation. An interface to lower-level (full) GP inference and prediction is provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided. For details and tutorial, see Gramacy (2016 <doi:10.18637/jss.v072.i01>.

Authors:Robert B. Gramacy <rbg@vt.edu>, Furong Sun <furongs@vt.edu>

laGP_1.5-9.tar.gz
laGP_1.5-9.tar.gz(r-4.5-noble)laGP_1.5-9.tar.gz(r-4.4-noble)
laGP_1.5-9.tgz(r-4.4-emscripten)laGP_1.5-9.tgz(r-4.3-emscripten)
laGP.pdf |laGP.html✨
laGP/json (API)

# Install 'laGP' in R:
install.packages('laGP', repos = 'https://cloud.r-project.org')
Uses libs:
  • openblas– Optimized BLAS
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

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

openblasopenmp

4.20 score 8 stars 2 packages 1.1k downloads 3 mentions 51 exports 4 dependencies

Last updated 2 years agofrom:e2ad6bdf6b. Checks:1 ERROR, 2 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesFAILMar 29 2025
R-4.5-linux-x86_64OKMar 29 2025
R-4.4-linux-x86_64OKMar 29 2025

Exports:aGPaGP.parallelaGP.RaGP.seqaGPsepaGPsep.RalcGPalcGPsepalcoptGPalcoptGPsepalcrayGPalcrayGPsepblhsblhs.loopdalcGPdalcGPsepdargdeleteGPdeleteGPsdeleteGPsepdeleteGPsepsdiscrep.estdistancefcalibfishGPgargieciGPieciGPsepjmleGPjmleGP.RjmleGPsepjmleGPsep.RlaGPlaGP.RlaGPseplaGPsep.RllikGPllikGPsepmleGPmleGPsepmleGPsep.RmspeGPnewGPnewGPsepoptim.auglagoptim.efipredGPpredGPseprandLineupdateGPupdateGPsep

Dependencies:clustermaptreerparttgp

a guide to the laGP package

Rendered fromlaGP.Rnwusingutils::Sweaveon Mar 29 2025.

Last update: 2022-06-27
Started: 2014-10-17

Citation

To cite laGP in publications use:

Robert B. Gramacy (2016). laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R. Journal of Statistical Software, 72(1), 1-46.<doi:10.18637/jss.v072.i01>

Corresponding BibTeX entry: 

  @Article{,
    title = {{laGP}: Large-Scale Spatial Modeling via Local Approximate
      Gaussian Processes in {R}},
    author = {Robert B. Gramacy},
    journal = {Journal of Statistical Software},
    year = {2016},
    volume = {72},
    number = {1},
    pages = {1--46},
    doi = {10.18637/jss.v072.i01},
  }