R package spectralGP: Approximate Gaussian processes using the Fourier basis. Routines for creating, manipulating, and performing Bayesian inference about Gaussian processes in one and two dimensions using the Fourier basis approximation: simulation and plotting of processes, calculation of coefficient variances, calculation of process density, coefficient proposals (for use in MCMC). It uses R environments to store GP objects as references/pointers.
Keywords for this software
References in zbMATH (referenced in 11 articles , 1 standard article )
Showing results 1 to 11 of 11.
- Seongil Jo; Taeryon Choi; Beomjo Park; Peter Lenk: bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors (2019) not zbMATH
- Hensman, James; Durrande, Nicolas; Solin, Arno: Variational Fourier features for Gaussian processes (2018)
- Barthelmé, Simon: Fast matrix computations for functional additive models (2015)
- Fabio Sigrist; Hans Künsch; Werner Stahel: spate: An R Package for Spatio-Temporal Modeling with a Stochastic Advection-Diffusion Process (2015) not zbMATH
- Paciorek, Christopher J.: Spatial models for point and areal data using Markov random fields on a fine grid (2013)
- Lemos, Ricardo T.; Sansó, Bruno: Conditionally linear models for non-homogeneous spatial random fields (2012)
- Haran, Murali: Gaussian random field models for spatial data (2011)
- Adrian Bowman; Iain Gibson; E. Scott; Ewan Crawford: Interactive Teaching Tools for Spatial Sampling (2010) not zbMATH
- Ruppert, David; Wand, M. P.; Carroll, Raymond J.: Semiparametric regression during 2003--2007 (2009)
- Crainiceanu, Ciprian M.; Diggle, Peter J.; Rowlingson, Barry: Bivariate binomial spatial modeling of Loa Loa prevalence in tropical africa (2008)
- Christopher Paciorek: Bayesian Smoothing with Gaussian Processes Using Fourier Basis Functions in the spectralGP Package (2007) not zbMATH