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tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models

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  • Gramacy, Robert B.

Abstract

The tgp package for R is a tool for fully Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes with jumps to the limiting linear model. Special cases also implemented include Bayesian linear models, linear CART, stationary separable and isotropic Gaussian processes. In addition to inference and posterior prediction, the package supports the (sequential) design of experiments under these models paired with several objective criteria. 1-d and 2-d plotting, with higher dimension projection and slice capabilities, and tree drawing functions (requiring maptree and combinat packages), are also provided for visualization of tgp objects.

Suggested Citation

  • Gramacy, Robert B., 2007. "tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 19(i09).
    • Handle: RePEc:jss:jstsof:v:019:i09
    DOI: http://hdl.handle.net/10.18637/jss.v019.i09
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    Cited by:

    1. Tamara Broderick & Robert Gramacy, 2011. "Classification and Categorical Inputs with Treed Gaussian Process Models," Journal of Classification, Springer;The Classification Society, vol. 28(2), pages 244-270, July.
    2. Matthew W. Wheeler, 2019. "Bayesian additive adaptive basis tensor product models for modeling high dimensional surfaces: an application to high‐throughput toxicity testing," Biometrics, The International Biometric Society, vol. 75(1), pages 193-201, March.
    3. Monterrubio-Gómez, Karla & Roininen, Lassi & Wade, Sara & Damoulas, Theodoros & Girolami, Mark, 2020. "Posterior inference for sparse hierarchical non-stationary models," Computational Statistics & Data Analysis, Elsevier, vol. 148(C).
    4. MacDonald, Blake & Ranjan, Pritam & Chipman, Hugh, 2015. "GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i12).
    5. repec:jss:jstsof:33:i06 is not listed on IDEAS
    6. Liukkonen, Lauri & Ayllón, Daniel & Kunnasranta, Mervi & Niemi, Marja & Nabe-Nielsen, Jacob & Grimm, Volker & Nyman, Anna-Maija, 2018. "Modelling movements of Saimaa ringed seals using an individual-based approach," Ecological Modelling, Elsevier, vol. 368(C), pages 321-335.
    7. Savitsky, Terrance D., 2016. "Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i02).
    8. Davis, Casey B. & Hans, Christopher M. & Santner, Thomas J., 2021. "Prediction of non-stationary response functions using a Bayesian composite Gaussian process," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).
    9. Gramacy, Robert B. & Taddy, Matthew Alan, 2010. "Categorical Inputs, Sensitivity Analysis, Optimization and Importance Tempering with tgp Version 2, an R Package for Treed Gaussian Process Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i06).
    10. Al Ali, Hannah & Daneshkhah, Alireza & Boutayeb, Abdesslam & Malunguza, Noble Jahalamajaha & Mukandavire, Zindoga, 2022. "Exploring dynamical properties of a Type 1 diabetes model using sensitivity approaches," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 324-342.
    11. Xiao, Qian & Xu, Hongquan, 2021. "A mapping-based universal Kriging model for order-of-addition experiments in drug combination studies," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    12. Nielsen, Julie K. & Tribuzio, Cindy A., 2023. "Development and parameterization of a data likelihood model for geolocation of a bentho-pelagic fish in the North Pacific Ocean," Ecological Modelling, Elsevier, vol. 478(C).
    13. Picheny, Victor & Ginsbourger, David, 2014. "Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1035-1053.
    14. Daniel W. Gladish & Daniel E. Pagendam & Luk J. M. Peeters & Petra M. Kuhnert & Jai Vaze, 2018. "Emulation Engines: Choice and Quantification of Uncertainty for Complex Hydrological Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(1), pages 39-62, March.
    15. Horiguchi, Akira & Pratola, Matthew T. & Santner, Thomas J., 2021. "Assessing variable activity for Bayesian regression trees," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    16. Samuel W. Malone & Robert B. Gramacy & Enrique Ter Horst, 2016. "Timing Foreign Exchange Markets," Econometrics, MDPI, vol. 4(1), pages 1-23, March.
    17. Erickson, Collin B. & Ankenman, Bruce E. & Sanchez, Susan M., 2018. "Comparison of Gaussian process modeling software," European Journal of Operational Research, Elsevier, vol. 266(1), pages 179-192.
    18. Gramacy, Robert B. & Lee, Herbert K.H., 2008. "Gaussian processes and limiting linear models," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 123-136, September.
    19. Waley W. J. Liang & Herbert K. H. Lee, 2019. "Bayesian nonstationary Gaussian process models via treed process convolutions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 797-818, September.
    20. Roustant, Olivier & Ginsbourger, David & Deville, Yves, 2012. "DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i01).

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