Gaussian Process Modelling

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)
• maximum likelihood hyper-parameter estimation
• McMC hyper-parameter sampling
• spatially varying covariance function
• Gaussian Process approximations (to deal with larger data set)
• add other covariance models

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</p>

Thank you!

How to install/load

1. Install R

Download R from the R Cran website and install it.

1. Optionally install a R-editor:
   • Notepad++ combined with NppToR
   • Rstudio
   • RKward
2. If necessary, learn some R basics:
   • An interactive introduction to R
   • Short R introduction
   • R course

2. Install a R-editor [optionally]

Possible R-editor choices:

• Rstudio (recommanded if you start)
• Notepad++ combined with NppToR
• RKward

3. Install & load GauProMod

if(!require("devtools")) install.packages("devtools")
devtools::install_github("emanuelhuber/GauProMod")
library(GauProMod)