README.md

In dirmeier/gpR: Gaussian processes in R

gpR

Gaussian processes for machine learning in R and FORTRAN.

Introduction

Gaussian Processes have recently gained a lot of attention in machine learning. gpR shows how the calculation of the posterior predictive of a Gaussian Process and prediction of novel data is done when the kernel parameters are known. In the next versions I will implement how those are calculated by optimizing the marginal likelihood and probably include more kernels.

Installation

Install gpR using:

devtools::install_github("dirmeier/gpR") 

from the R-console.

Usage

Load the package using library(gpR). We provide a vignette for the package that can be called using: vignette("gpR"). This should be all the information you need. For regression try the demo-tour using:

demo.regression()

or for classification (i.e. binomial responses):

demo.bin.classification()

Also check out the source code for more info, fork the package, or just write me!

Author

• Simon Dirmeier [e-mail]

dirmeier/gpR documentation built on May 15, 2019, 8:50 a.m.