Project: Gaussian Process Modelling

Abstract: Software package for Gaussian Process (GP) modelling written in R language. The core functions are coded in C++ and based on the EIGEN library (through RcppEigen).

R functions for Gaussian process (GP) modelling. The core functions are coded in C++ and based on the EIGEN library (through RcppEigen) Notes

Current features:

• Posterior Gaussian Process with Gaussian likelihood (Gaussian process conditioned to noise-free and noisy observations)
• Space-time Gaussian process
• Gaussian Process with monomial mean functions with vague Gaussian prior on the coefficient parameters
• Gaussian Process conditioned to derivative observations
• Covariance function: Matern, Gaussian, linear
• Anisotropic covariance functions (scale and rotation)
• Log marginal likelihood of the Gaussian process
• Cross-matrix distance (distance between every rows of each matrix): crossdist(x,y,M) (with M a positive semidefinite matrix for anisotropic distances)

This is an ongoing project. If you have any questions, requirements, suggestions, don’t hesitate to contact me (in english, french or german): emanuel.huber@alumni.ethz.ch