Package: hetGP 1.1.7

hetGP: Heteroskedastic Gaussian Process Modeling and Design under Replication

Performs Gaussian process regression with heteroskedastic noise following the model by Binois, M., Gramacy, R., Ludkovski, M. (2016) <doi:10.48550/arXiv.1611.05902>, with implementation details in Binois, M. & Gramacy, R. B. (2021) <doi:10.18637/jss.v098.i13>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.

Authors:Mickael Binois [aut, cre], Robert B. Gramacy [aut]

hetGP_1.1.7.tar.gz
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hetGP.pdf |hetGP.html✨
hetGP/json (API)
NEWS

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

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • X - Assemble To Order (ATO) Data and Fits
  • Xa - Assemble To Order (ATO) Data and Fits
  • Xtest - Assemble To Order (ATO) Data and Fits
  • Xtrain - Assemble To Order (ATO) Data and Fits
  • Xtrain.out - Assemble To Order (ATO) Data and Fits
  • Z - Assemble To Order (ATO) Data and Fits
  • Za - Assemble To Order (ATO) Data and Fits
  • Zm - Assemble To Order (ATO) Data and Fits
  • Ztest - Assemble To Order (ATO) Data and Fits
  • Ztrain - Assemble To Order (ATO) Data and Fits
  • Ztrain.out - Assemble To Order (ATO) Data and Fits
  • Zv - Assemble To Order (ATO) Data and Fits
  • ato.a - Assemble To Order (ATO) Data and Fits
  • bfs.exp - Bayes Factor Data
  • bfs.gamma - Bayes Factor Data
  • kill - Assemble To Order (ATO) Data and Fits
  • mult - Assemble To Order (ATO) Data and Fits
  • nc - Assemble To Order (ATO) Data and Fits
  • out - Assemble To Order (ATO) Data and Fits
  • out.a - Assemble To Order (ATO) Data and Fits
  • train - Assemble To Order (ATO) Data and Fits

On CRAN:

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

4.86 score 5 stars 2 packages 242 scripts 354 downloads 38 exports 3 dependencies

Last updated 3 months agofrom:728a973673. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 04 2024
R-4.5-win-x86_64OKNov 04 2024
R-4.5-linux-x86_64OKNov 04 2024
R-4.4-win-x86_64OKNov 04 2024
R-4.4-mac-x86_64OKNov 04 2024
R-4.4-mac-aarch64OKNov 04 2024
R-4.3-win-x86_64OKNov 04 2024
R-4.3-mac-x86_64OKNov 04 2024
R-4.3-mac-aarch64OKNov 04 2024

Exports:allocate_multcompareGPcov_gencrit_cSURcrit_EIcrit_ICUcrit_IMSPEcrit_logEIcrit_MCUcrit_MEEcrit_optimcrit_qEIcrit_tMSEderiv_crit_EIderiv_crit_IMSPEf1df1d_nf1d2f1d2_nfind_repshorizonIMSPEIMSPE_optimlogLikHLOO_predsmleCRNGPmleHetGPmleHetTPmleHomGPmleHomTPpred_noisy_inputrebuildscoressimulsirEvalsirSimulatestripWij

Dependencies:DiceDesignMASSRcpp

a guide to the hetGP package

Rendered fromhetGP_vignette.Rnwusingknitr::knitron Nov 04 2024.

Last update: 2021-03-17
Started: 2019-01-10

Readme and manuals

Help Manual

Help pageTopics
Allocation of replicates on existing designsallocate_mult
Assemble To Order (ATO) Data and Fitsato ato.a kill mult nc out out.a reps train X Xa Xtest Xtrain Xtrain.out Z Za Zm Ztest Ztrain Ztrain.out Zv
Bayes Factor Databfs bfs.exp bfs.gamma
Likelihood-based comparison of modelscompareGP
Correlation function of selected type, supporting both isotropic and product formscov_gen
Contour Stepwise Uncertainty Reduction criterioncrit_cSUR
Expected Improvement criterioncrit_EI
Integrated Contour Uncertainty criterioncrit_ICU
Sequential IMSPE criterioncrit_IMSPE
Logarithm of Expected Improvement criterioncrit_logEI
Maximum Contour Uncertainty criterioncrit_MCU
Maximum Empirical Error criterioncrit_MEE
Criterion optimizationcrit_optim
Parallel Expected improvementcrit_qEI
t-MSE criterioncrit_tMSE
Derivative of EI criterion for GP modelsderiv_crit_EI
Derivative of crit_IMSPEderiv_crit_IMSPE
1d test function (1)f1d
Noisy 1d test function (1) Add Gaussian noise with variance r(x) = scale * (1.1 + sin(2 pi x))^2 to 'f1d'f1d_n
1d test function (2)f1d2
Noisy 1d test function (2) Add Gaussian noise with variance r(x) = scale * (exp(sin(2 pi x)))^2 to 'f1d2'f1d2_n
Data preprocessingfind_reps
Adapt horizonhorizon
Integrated Mean Square Prediction ErrorIMSPE
IMSPE optimizationIMSPE_optim
Leave one out predictionsLOO_preds
Gaussian process modeling with correlated noisemleCRNGP
Gaussian process modeling with heteroskedastic noisemleHetGP
Student-t process modeling with heteroskedastic noisemleHetTP
Gaussian process modeling with homoskedastic noisemleHomGP
Student-T process modeling with homoskedastic noisemleHomTP
Gaussian process prediction prediction at a noisy input 'x', with centered Gaussian noise of variance 'sigma_x'. Several options are available, with different efficiency/accuracy tradeoffs.pred_noisy_input
Gaussian process predictions using a GP object for correlated noise (of class 'CRNGP')predict.CRNGP
Gaussian process predictions using a heterogeneous noise GP object (of class 'hetGP')predict.hetGP
Student-t process predictions using a heterogeneous noise TP object (of class 'hetTP')predict.hetTP
Gaussian process predictions using a homoskedastic noise GP object (of class 'homGP')predict.homGP
Student-t process predictions using a homoskedastic noise GP object (of class 'homGP')predict.homTP
Import and export of hetGP objectsrebuild rebuild.hetGP rebuild.hetTP rebuild.homGP rebuild.homTP strip
Score and RMSE function To asses the performance of the prediction, this function computes the root mean squared error and proper score function (also known as negative log-probability density).scores
Conditional simulation for CRNGPsimul
Fast conditional simulation for a CRNGP modelsimul.CRNGP
SIR test problemsirEval sirSimulate
Update '"hetGP"'-class model fit with new observationsupdate.hetGP
Update '"hetTP"'-class model fit with new observationsupdate.hetTP
Fast 'homGP'-updateupdate.homGP
Fast 'homTP'-updateupdate.homTP
Compute double integral of the covariance kernel over a [0,1]^d domainWij