{"id":16599986,"url":"https://github.com/dirmeier/gpr","last_synced_at":"2025-06-18T01:35:05.424Z","repository":{"id":133195402,"uuid":"67435892","full_name":"dirmeier/gpR","owner":"dirmeier","description":"Gaussian processes for machine learning in R and FORTRAN.","archived":false,"fork":false,"pushed_at":"2017-05-26T21:48:31.000Z","size":577,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-08T10:59:15.928Z","etag":null,"topics":["gaussian-processes","machine-learning","toy-project"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dirmeier.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-09-05T16:30:31.000Z","updated_at":"2022-09-30T07:34:01.000Z","dependencies_parsed_at":"2023-03-14T21:00:40.517Z","dependency_job_id":null,"html_url":"https://github.com/dirmeier/gpR","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/dirmeier/gpR","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dirmeier%2FgpR","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dirmeier%2FgpR/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dirmeier%2FgpR/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dirmeier%2FgpR/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dirmeier","download_url":"https://codeload.github.com/dirmeier/gpR/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dirmeier%2FgpR/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":260468441,"owners_count":23013916,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["gaussian-processes","machine-learning","toy-project"],"created_at":"2024-10-12T00:13:12.934Z","updated_at":"2025-06-18T01:35:00.388Z","avatar_url":"https://github.com/dirmeier.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch1 align=\"center\"\u003e gpR \u003c/h1\u003e\n\n[![Project Status](http://www.repostatus.org/badges/latest/inactive.svg)](http://www.repostatus.org/#inactive)\n[![Build Status](https://travis-ci.org/dirmeier/gpR.svg?branch=master)](https://travis-ci.org/dirmeier/gpR)\n[![codecov](https://codecov.io/gh/dirmeier/gpR/branch/master/graph/badge.svg)](https://codecov.io/gh/dirmeier/gpR)\n\nGaussian processes for machine learning in R and FORTRAN.\n\n## Introduction\n\nGaussian Processes have recently gained a lot of attention in machine learning. \u003ccode\u003egpR\u003c/code\u003e 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.\n\n## Installation\n \nInstall `gpR` using:\n\n```{r}\ndevtools::install_github(\"dirmeier/gpR\") \n```\n\nfrom the R-console.\n\n## Usage\n\nLoad 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:\n\n```{r}\ndemo.regression()\n```\n\nor for classification (i.e. binomial responses):\n\n```{r}\ndemo.bin.classification()\n```\n\nAlso check out the source code for more info, fork the package, or just write me!\n\n## Author\n\n* Simon Dirmeier \u003ca href=\"mailto:simon.dirmeier@gmx.de\"\u003esimon.dirmeier@gmx.de\u003c/a\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdirmeier%2Fgpr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdirmeier%2Fgpr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdirmeier%2Fgpr/lists"}