R package tgp: Bayesian treed Gaussian process models. Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes (GPs) with jumps to the limiting linear model (LLM). Special cases also implemented include Bayesian linear models, CART, treed linear models, stationary separable and isotropic GPs, and GP single-index models. Provides 1-d and 2-d plotting functions (with projection and slice capabilities) and tree drawing, designed for visualization of tgp-class output. Sensitivity analysis and multi-resolution models are supported. Sequential experimental design and adaptive sampling functions are also provided, including ALM, ALC, and expected improvement. The latter supports derivative-free optimization of noisy black-box functions.

References in zbMATH (referenced in 19 articles , 1 standard article )

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  1. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  2. Gladish, Daniel W.; Pagendam, Daniel E.; Peeters, Luk J. M.; Kuhnert, Petra M.; Vaze, Jai: Emulation engines: choice and quantification of uncertainty for complex hydrological models (2018)
  3. Marmin, SĂ©bastien; Ginsbourger, David; Baccou, Jean; Liandrat, Jacques: Warped Gaussian processes and derivative-based sequential designs for functions with heterogeneous variations (2018)
  4. Hu, Ruimeng; Ludkovsk, Mike: Sequential design for ranking response surfaces (2017)
  5. Guhaniyogi, Rajarshi; Dunson, David B.: Compressed Gaussian process for manifold regression (2016)
  6. Kang, Lulu; Joseph, V. Roshan: Kernel approximation: from regression to interpolation (2016)
  7. Robert Gramacy: laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R (2016)
  8. Terrance Savitsky: Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R (2016)
  9. Blake MacDonald; Pritam Ranjan; Hugh Chipman: GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs (2015)
  10. Low-Kam, Cecile; Telesca, Donatello; Ji, Zhaoxia; Zhang, Haiyuan; Xia, Tian; Zink, Jeffrey I.; Nel, Andre E.: A Bayesian regression tree approach to identify the effect of nanoparticles’ properties on toxicity profiles (2015)
  11. Blake MacDoanld, Hugh Chipman, Pritam Ranjan: GPfit: An R package for Gaussian Process Model Fitting using a New Optimization Algorithm (2013) arXiv
  12. Gramacy, Robert B.; Taddy, Matt; Wild, Stefan M.: Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning (2013)
  13. Gramacy, Robert B.; Lee, Herbert K. H.: Cases for the nugget in modeling computer experiments (2012)
  14. Olivier Roustant; David Ginsbourger; Yves Deville: DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization (2012)
  15. Broderick, Tamara; Gramacy, Robert B.: Classification and categorical inputs with treed Gaussian process models (2011)
  16. Lee, Herbert K. H.; Gramacy, Robert B.; Linkletter, Crystal; Gray, Genetha A.: Optimization subject to hidden constraints via statistical emulation (2011)
  17. Robert Gramacy; Matthew Taddy: Categorical Inputs, Sensitivity Analysis, Optimization and Importance Tempering with tgp Version 2, an R Package for Treed Gaussian Process Models (2010)
  18. Gramacy, Robert B.; Lee, Herbert K. H.: Gaussian processes and limiting linear models (2008)
  19. Robert Gramacy: tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models (2007)