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
References in zbMATH (referenced in 12 articles )
Showing results 1 to 12 of 12.
- Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
- Beck, Joakim; Guillas, Serge: Sequential design with mutual information for computer experiments (MICE): emulation of a tsunami model (2016)
- Pokharel, Gyanendra; Deardon, Rob: Gaussian process emulators for spatial individual-level models of infectious disease (2016)
- Robert Gramacy: laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R (2016)
- Blake MacDonald; Pritam Ranjan; Hugh Chipman: GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs (2015)
- Christopher Paciorek; Benjamin Lipshitz; Wei Zhuo; Prabhat; Cari G. Kaufman; Rollin Thomas: Parallelizing Gaussian Process Calculations in R (2015)
- Regier, Jeffrey C.; Stark, Philip B.: Mini-minimax uncertainty quantification for emulators (2015)
- Blake MacDoanld, Hugh Chipman, Pritam Ranjan: GPfit: An R package for Gaussian Process Model Fitting using a New Optimization Algorithm (2013) arXiv
- Olivier Roustant; David Ginsbourger; Yves Deville: DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization (2012)
- Paulo, Rui; García-Donato, Gonzalo; Palomo, Jesús: Calibration of computer models with multivariate output (2012)
- Haaland, Ben; Qian, Peter Z.G.: Accurate emulators for large-scale computer experiments (2011)
- Dancik, Garrett M.; Dorman, Karin S.: \itmlegp: Statistical analysis for computer models of biological systems using R. (2008) ioport