https://github.com/dirmeier/gpr

Gaussian processes for machine learning in R and FORTRAN.
https://github.com/dirmeier/gpr

gaussian-processes machine-learning toy-project

Last synced: about 1 year ago
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Gaussian processes for machine learning in R and FORTRAN.

• Host: GitHub
• URL: https://github.com/dirmeier/gpr
• Owner: dirmeier
• License: gpl-3.0
• Created: 2016-09-05T16:30:31.000Z (almost 10 years ago)
• Default Branch: master
• Last Pushed: 2017-05-26T21:48:31.000Z (about 9 years ago)
• Last Synced: 2025-03-08T10:59:15.928Z (over 1 year ago)
• Topics: gaussian-processes, machine-learning, toy-project
• Language: R
• Homepage:
• Size: 563 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
 gpR 


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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:

```{r}

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:

```{r}

demo.regression()

```

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

```{r}

demo.bin.classification()

```

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

## Author

* Simon Dirmeier simon.dirmeier@gmx.de