mlegp

R package mlegp: Maximum Likelihood Estimates of Gaussian Processes. Maximum likelihood Gaussian process modeling for univariate and multi-dimensional outputs with diagnostic plots. Contact the maintainer for a package version that implements sensitivity analysis functionality.

Keywords for this software

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References in zbMATH (referenced in 20 articles )

Showing results 1 to 20 of 20.
Sorted by year (citations)

  1. Evandro Konzen, Yafeng Cheng, Jian Qing Shi: Gaussian Process for Functional Data Analysis: The GPFDA Package for R (2021) arXiv
  2. Huang, Chaofan; Ren, Yi; McGuinness, Emily K.; Losego, Mark D.; Lively, Ryan P.; Joseph, V. Roshan: Bayesian optimization of functional output in inverse problems (2021)
  3. Mickael Binois, Robert B. Gramacy: hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R (2021) not zbMATH
  4. Wang, Wenjia: On the inference of applying Gaussian process modeling to a deterministic function (2021)
  5. Sung, Chih-Li; Wang, Wenjia; Plumlee, Matthew; Haaland, Benjamin: Multiresolution functional ANOVA for large-scale, many-input computer experiments (2020)
  6. Yang, F.; Lin, C. Devon; Ranjan, P.: Global fitting of the response surface via estimating multiple contours of a simulator (2020)
  7. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  8. Haaland, Benjamin; Wang, Wenjia; Maheshwari, Vaibhav: A framework for controlling sources of inaccuracy in Gaussian process emulation of deterministic computer experiments (2018)
  9. Beck, Joakim; Guillas, Serge: Sequential design with mutual information for computer experiments (MICE): emulation of a tsunami model (2016)
  10. Pokharel, Gyanendra; Deardon, Rob: Gaussian process emulators for spatial individual-level models of infectious disease (2016)
  11. Robert Gramacy: laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R (2016) not zbMATH
  12. Blake MacDonald; Pritam Ranjan; Hugh Chipman: GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs (2015) not zbMATH
  13. Christopher Paciorek; Benjamin Lipshitz; Wei Zhuo; Prabhat; Cari G. Kaufman; Rollin Thomas: Parallelizing Gaussian Process Calculations in R (2015) not zbMATH
  14. Regier, Jeffrey C.; Stark, Philip B.: Mini-minimax uncertainty quantification for emulators (2015)
  15. Picheny, Victor; Ginsbourger, David: Noisy kriging-based optimization methods: a unified implementation within the DiceOptim package (2014)
  16. Blake MacDoanld, Hugh Chipman, Pritam Ranjan: GPfit: An R package for Gaussian Process Model Fitting using a New Optimization Algorithm (2013) arXiv
  17. Olivier Roustant; David Ginsbourger; Yves Deville: DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization (2012) not zbMATH
  18. Paulo, Rui; García-Donato, Gonzalo; Palomo, Jesús: Calibration of computer models with multivariate output (2012)
  19. Haaland, Ben; Qian, Peter Z. G.: Accurate emulators for large-scale computer experiments (2011)
  20. Dancik, Garrett M.; Dorman, Karin S.: \textitmlegp: Statistical analysis for computer models of biological systems using R. (2008) ioport