{"id":17586851,"url":"https://github.com/aebilgrau/gmcm","last_synced_at":"2025-07-15T07:34:10.471Z","repository":{"id":13284635,"uuid":"15970417","full_name":"AEBilgrau/GMCM","owner":"AEBilgrau","description":"Unsupervised Clustering and Meta-analysis using Gaussian Mixture Copula Models","archived":false,"fork":false,"pushed_at":"2021-11-04T22:29:13.000Z","size":26920,"stargazers_count":15,"open_issues_count":23,"forks_count":3,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-03-29T01:34:25.728Z","etag":null,"topics":["clustering","cran","gaussian-mixture-models","meta-analysis","r","rank","unsupervised-cluster-analysis"],"latest_commit_sha":null,"homepage":"http://AEBilgrau.github.io/GMCM","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AEBilgrau.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2014-01-16T14:41:13.000Z","updated_at":"2024-08-22T14:53:32.000Z","dependencies_parsed_at":"2022-08-19T06:00:35.994Z","dependency_job_id":null,"html_url":"https://github.com/AEBilgrau/GMCM","commit_stats":null,"previous_names":[],"tags_count":13,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AEBilgrau%2FGMCM","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AEBilgrau%2FGMCM/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AEBilgrau%2FGMCM/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AEBilgrau%2FGMCM/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AEBilgrau","download_url":"https://codeload.github.com/AEBilgrau/GMCM/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":249157374,"owners_count":21221937,"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":["clustering","cran","gaussian-mixture-models","meta-analysis","r","rank","unsupervised-cluster-analysis"],"created_at":"2024-10-22T03:06:33.698Z","updated_at":"2025-04-15T21:34:32.083Z","avatar_url":"https://github.com/AEBilgrau.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"\r\n[![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active)\r\n[![Build Status](https://api.travis-ci.org/AEBilgrau/GMCM.svg?branch=master)](https://travis-ci.org/AEBilgrau/GMCM)\r\n[![CRAN version](http://www.r-pkg.org/badges/version/GMCM)](https://cran.r-project.org/package=GMCM)\r\n[![CRAN RStudio mirror downloads](http://cranlogs.r-pkg.org/badges/GMCM)](https://cran.r-project.org/package=GMCM)\r\n[![Total CRAN downloads](http://cranlogs.r-pkg.org/badges/grand-total/GMCM)](https://cran.r-project.org/package=GMCM)\r\n[![Coverage Status](https://coveralls.io/repos/github/AEBilgrau/GMCM/badge.svg?branch=master)](https://coveralls.io/github/AEBilgrau/GMCM?branch=master)\r\n\r\nGMCM\r\n----\r\n### Fast estimation of Gaussian Mixture Copula Models\r\n\r\nThe [**GMCM** package](https://cran.r-project.org/package=GMCM) \r\n([Bilgrau et. al., 2016](https://www.jstatsoft.org/article/view/v070i02))\r\noffers R functions that very fast perform high-dimensional meta-analysis \r\n[(Li et. al., 2011)](http://arxiv.org/pdf/1110.4705.pdf) \r\nand general unsupervised cluster analysis \r\n[(Tewari et. al., 2011)](http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6137392) \r\nusing Gaussian Copula Mixture Models. \r\nOnline documentation is available [here.](http://AEBilgrau.github.io/GMCM)\r\n\r\nGaussian copula mixture models (GMCMs) are a very flexible alternative to regular Gaussian mixture models (GMMs) in unsupervised cluster analysis of continuous data where non-normal clusters are present. \r\nGMCMs models the *ranks* of the observed data and are thus invariant to monotone increasing transformations of the data, i.e. they are semi-parametric and only the ordering of the data is important providing needed flexibility. \r\nA special-case of GMCMs can be used for a novel meta-analysis approach in high-dimensional settings. \r\nIn this context, the model tries to cluster results into two groups which agree and do not agree on statistical evidence. These two groups corresponds to a reproducible and irreproducible group.\r\n\r\nThe optimization of the complicated likelihood function is difficult, however. \r\n**GMCM** utilizes \r\n[**Rcpp**](https://github.com/RcppCore/Rcpp) \r\nand \r\n[**RcppArmadillo**](https://github.com/RcppCore/RcppArmadillo) \r\nto evaluate the likelihood function quickly and arrive at a parameter estimate using either standard numerical optimization routines or an pseudo EM algorithm.\r\n\r\nAdditional information, documentation, help, and examples can be found by [here](https://aebilgrau.github.io/GMCM\r\n) or by running `?GMCM` in **R**. \r\nThe paper[1] is also found as a vignette by `vignette(\"GMCM-JStatSoft\")`  or [the official website online.](https://www.jstatsoft.org/article/view/v070i02).\r\nThe core user functions of **GMCM** are `fit.full.GMCM` and `fit.meta.GMCM`.\r\n\r\n## Installation\r\n\r\nThe released and tested version of **GMCM** is available at\r\n[CRAN](https://cran.r-project.org/package=GMCM) \r\n(Comprehensive R Archive Network).\r\nIt can be installed from within R by running \r\n\r\n```R\r\ninstall.packages(\"GMCM\")\r\n```\r\n\r\nIf you wish to install the latest version of **GMCM** directly from the master branch at GitHub, run \r\n\r\n```R\r\n#install.packages(\"remotes\")  # Install remotes if needed\r\nremotes::install_github(\"AEBilgrau/GMCM\")\r\n```\r\n\r\nNote, that this version is in development and is likely different from the version at CRAN. \r\nAs such, it may be unstable. Be sure that you have the \r\n[package development prerequisites](https://support.rstudio.com/hc/en-us/articles/200486498-Package-Development-Prerequisites) \r\nif you wish to install the package from the source.\r\n\r\nWhen installed, run `GMCM::runGMCM()` to launch a local instance of the GMCM shiny application also available [online at shinyapps.io](https://gmcm.shinyapps.io/GMCM/).\r\nRun `news(package = \"GMCM\")` to view the latest changes of GMCM or visit [here](http://AEBilgrau.github.io/GMCM/news).\r\n\r\nFor previous versions of **GMCM**, visit the old [releases at GitHub](https://github.com/AEBilgrau/GMCM/releases) or the [archive at CRAN.](https://cran.r-project.org/src/contrib/Archive/GMCM/)\r\n\r\n\r\n## Usage\r\nAs noted above, the usage of GMCM comes in two different applications; one general and one special.\r\n\r\n#### Special GMCMs\r\nAn example of using the package to fit special GMCMs for meta analysis of is described here `vignette(\"usage-example-special-model\")`. This model is a specific special case of the general GMCMs. \r\n\r\n#### General GMCMs\r\nAn example of unsupervised clustering using the package is found with `vignette(\"usage-example-general-model\")` for general purposes. \r\n\r\n#### Shiny\r\nThe package also provides a graphical user interface via Shiny for both its uses. See\r\n`vignette(\"usage-shiny-graphical-interface\")`.\r\n\r\n\r\n## References\r\n\r\n  1. Anders Ellern Bilgrau, Poul Svante Eriksen, Jakob Gulddahl Rasmussen, Hans \r\n     Erik Johnsen, Karen Dybkaer, Martin Boegsted (2016). **GMCM: Unsupervised \r\n     Clustering and Meta-Analysis Using Gaussian Mixture Copula Models.** \r\n     Journal of Statistical Software, 70(2), 1-23. [doi:10.18637/jss.v070.i02](http://doi.org/10.18637/jss.v070.i02)\r\n\r\n---\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faebilgrau%2Fgmcm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faebilgrau%2Fgmcm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faebilgrau%2Fgmcm/lists"}