Package: CGGP 1.0.4.9000

CGGP: Composite Grid Gaussian Processes

Run computer experiments using the adaptive composite grid algorithm with a Gaussian process model. The algorithm works best when running an experiment that can evaluate thousands of points from a deterministic computer simulation. This package is an implementation of a forthcoming paper by Plumlee, Erickson, Ankenman, et al. For a preprint of the paper, contact the maintainer of this package.

Authors:Collin Erickson [aut, cre], Matthew Plumlee [aut]

CGGP_1.0.4.9000.tar.gz
CGGP_1.0.4.9000.zip(r-4.5)CGGP_1.0.4.9000.zip(r-4.4)CGGP_1.0.4.9000.zip(r-4.3)
CGGP_1.0.4.9000.tgz(r-4.4-x86_64)CGGP_1.0.4.9000.tgz(r-4.4-arm64)CGGP_1.0.4.9000.tgz(r-4.3-x86_64)CGGP_1.0.4.9000.tgz(r-4.3-arm64)
CGGP_1.0.4.9000.tar.gz(r-4.5-noble)CGGP_1.0.4.9000.tar.gz(r-4.4-noble)
CGGP_1.0.4.9000.tgz(r-4.4-emscripten)CGGP_1.0.4.9000.tgz(r-4.3-emscripten)
CGGP.pdf |CGGP.html
CGGP/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/collinerickson/cggp/issues

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

On CRAN:

39 exports 2 stars 1.11 score 2 dependencies 13 scripts 254 downloads

Last updated 8 months agofrom:51db6eba82. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 22 2024
R-4.5-win-x86_64OKAug 22 2024
R-4.5-linux-x86_64OKAug 22 2024
R-4.4-win-x86_64OKAug 22 2024
R-4.4-mac-x86_64OKAug 22 2024
R-4.4-mac-aarch64OKAug 22 2024
R-4.3-win-x86_64OKAug 22 2024
R-4.3-mac-x86_64OKAug 22 2024
R-4.3-mac-aarch64OKAug 22 2024

Exports:CGGP_internal_calcMSECGGP_internal_calcMSEdeCGGP_internal_calcpwCGGP_internal_calcpwanddpwCGGP_internal_CorrMatCauchyCGGP_internal_CorrMatCauchySQCGGP_internal_CorrMatCauchySQTCGGP_internal_CorrMatGaussianCGGP_internal_CorrMatMatern32CGGP_internal_CorrMatMatern52CGGP_internal_CorrMatPowerExpCGGP_internal_CorrMatWendland0CGGP_internal_CorrMatWendland1CGGP_internal_CorrMatWendland2CGGP_internal_gneglogpostCGGP_internal_MSEpredcalcCGGP_internal_neglogpostCGGP_internal_set_corrCGGPappendCGGPcreateCGGPfitCGGPplotblocksCGGPplotblockselectionCGGPplotcorrCGGPplotheatCGGPplothistCGGPplotsamplesneglogpostCGGPplotsliceCGGPplotthetaCGGPplotvariogramCGGPpredCGGPvalplotCGGPvalstatsrcpp_fastmatclcrrcpp_fastmatclcranddclcrrcpp_gkronDBSrcpp_kronDBSvalplotvalstats

Dependencies:RcppRcppArmadillo

An Introduction to the CGGP Package

Rendered fromCGGP.Rmdusingknitr::rmarkdownon Aug 22 2024.

Last update: 2019-06-10
Started: 2019-05-19

Readme and manuals

Help Manual

Help pageTopics
CGGP: A package for running sparse grid computer experimentsCGGP-package CGGP
Calculate MSE over single dimensionCGGP_internal_calcMSE
Calculate MSE over blocksCGGP_internal_calcMSEde
Calculate predictive weights for CGGPCGGP_internal_calcpw
Calculate derivative of pwCGGP_internal_calcpwanddpw
Cauchy correlation functionCGGP_internal_CorrMatCauchy
CauchySQ correlation functionCGGP_internal_CorrMatCauchySQ
CauchySQT correlation functionCGGP_internal_CorrMatCauchySQT
Gaussian correlation functionCGGP_internal_CorrMatGaussian
Matern 3/2 correlation functionCGGP_internal_CorrMatMatern32
Matern 5/2 correlation functionCGGP_internal_CorrMatMatern52
Power exponential correlation functionCGGP_internal_CorrMatPowerExp
Wendland0 (Triangle) correlation functionCGGP_internal_CorrMatWendland0
Wendland1 1 correlation functionCGGP_internal_CorrMatWendland1
Wendland2 2 correlation functionCGGP_internal_CorrMatWendland2
Gradient of negative log likelihood posteriorCGGP_internal_gneglogpost
Calculate MSE prediction along a single dimensionCGGP_internal_MSEpredcalc
Calculate negative log posteriorCGGP_internal_neglogpost
Set correlation function of CGGP objectCGGP_internal_set_corr
Add points to CGGPCGGPappend
Create sparse grid GPCGGPcreate
Update CGGP model given dataCGGPfit
CGGP block plotCGGPplotblocks
Plot CGGP block selection over timeCGGPplotblockselection
Plot correlation samplesCGGPplotcorr
Heatmap of SG design depthCGGPplotheat
Histogram of measurements at each design depth of each input dimensionCGGPplothist
Plot negative log posterior likelihood of samplesCGGPplotsamplesneglogpost
CGGP slice plotCGGPplotslice
Plot theta samplesCGGPplottheta
Plot something similar to a semivariogramCGGPplotvariogram
Plot validation prediction errors for CGGP objectCGGPvalplot
Calculate stats for CGGP prediction on validation dataCGGPvalstats
S3 plot method for CGGPplot.CGGP
S3 predict method for CGGPCGGPpred predict.CGGP
Print CGGP objectprint.CGGP
rcpp_fastmatclcrrcpp_fastmatclcr
rcpp_fastmatclcranddclcrrcpp_fastmatclcranddclcr
rcpp_kronDBSrcpp_gkronDBS
rcpp_kronDBSrcpp_kronDBS
Plot validation prediction errorsvalplot
Calculate stats for prediction on validation datavalstats