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.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
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- 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)
- 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)