{"id":17153079,"url":"https://github.com/itsrainingdata/ccdralgorithm","last_synced_at":"2025-04-13T12:43:51.587Z","repository":{"id":56937269,"uuid":"53634305","full_name":"itsrainingdata/ccdrAlgorithm","owner":"itsrainingdata","description":"Structure learning for Bayesian networks using the CCDr algorithm.","archived":false,"fork":false,"pushed_at":"2024-01-10T09:00:43.000Z","size":305,"stargazers_count":6,"open_issues_count":1,"forks_count":9,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-03-27T03:51:12.638Z","etag":null,"topics":["bayesian-networks","experimental-data","graphical-models","machine-learning-algorithms","r","regularization","statistical-learning"],"latest_commit_sha":null,"homepage":"","language":"C++","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/itsrainingdata.png","metadata":{"files":{"readme":"README.Rmd","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,"governance":null,"roadmap":null,"authors":null}},"created_at":"2016-03-11T02:45:58.000Z","updated_at":"2022-08-20T01:36:37.000Z","dependencies_parsed_at":"2024-01-16T22:18:37.226Z","dependency_job_id":"ba7e8efd-b24d-4579-8013-acd10b0d6475","html_url":"https://github.com/itsrainingdata/ccdrAlgorithm","commit_stats":{"total_commits":290,"total_committers":15,"mean_commits":"19.333333333333332","dds":0.5551724137931034,"last_synced_commit":"717d51c3fde3188c0cdbcf0569e2e9a4a8537b97"},"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/itsrainingdata%2FccdrAlgorithm","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/itsrainingdata%2FccdrAlgorithm/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/itsrainingdata%2FccdrAlgorithm/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/itsrainingdata%2FccdrAlgorithm/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/itsrainingdata","download_url":"https://codeload.github.com/itsrainingdata/ccdrAlgorithm/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248717240,"owners_count":21150387,"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":["bayesian-networks","experimental-data","graphical-models","machine-learning-algorithms","r","regularization","statistical-learning"],"created_at":"2024-10-14T21:45:10.713Z","updated_at":"2025-04-13T12:43:51.558Z","avatar_url":"https://github.com/itsrainingdata.png","language":"C++","readme":"---\noutput:\n  md_document:\n    variant: markdown_github\n---\n\n```{r, echo = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"README-\"\n)\n```\n\n# ccdrAlgorithm\n\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)\n[![Travis-CI Build Status](https://travis-ci.org/itsrainingdata/ccdrAlgorithm.svg?branch=master)](https://travis-ci.org/itsrainingdata/ccdrAlgorithm)\n[![](http://www.r-pkg.org/badges/version/ccdrAlgorithm)](http://www.r-pkg.org/pkg/ccdrAlgorithm)\n[![CRAN RStudio mirror downloads](http://cranlogs.r-pkg.org/badges/ccdrAlgorithm)](http://www.r-pkg.org/pkg/ccdrAlgorithm)\n\n`ccdrAlgorithm` implements the CCDr structure learning algorithm described in [[1-2](#references)]. This algorithm estimates the structure of a Bayesian network from mixed observational and experimental data using penalized maximum likelihood based on L1 or concave (MCP) regularization.\n\nPresently, this package implements the main algorithm and provides a method to simulate data from a Gaussian Bayesian network. To simulate random networks, it is recommended to use the [`sparsebnUtils`](https://cran.r-project.org/package=sparsebnUtils) package. Other packages for simulating DAGs and observational data include [`bnlearn`](https://cran.r-project.org/package=bnlearn), [`pcalg`](https://cran.r-project.org/package=pcalg), and [`igraph`](https://cran.r-project.org/package=igraph).\n\n## Overview\n\nThe main method is `ccdr.run`, which runs the CCDr structure learning algorithm as described in [[1-2](#references)]. For simulating data from a Gaussian Bayesian network, the package provides the method `generate_mvn_data`. This method can simulate observational data or experimental data with interventions (or combinations of both).\n\n## Installation\n\nYou can install:\n\n* the latest CRAN version with\n\n    ```R\n    install.packages(\"ccdrAlgorithm\")\n    ````\n\n* the latest development version from GitHub with\n\n    ```R\n    devtools::install_github(c(\"itsrainingdata/sparsebnUtils/dev\", \"itsrainingdata/ccdrAlgorithm/dev\"))\n    ```\n\n## References\n\n[1] Aragam, B. and Zhou, Q. (2015). [Concave penalized estimation of sparse Gaussian Bayesian networks.](http://jmlr.org/papers/v16/aragam15a.html) _The Journal of Machine Learning Research_. 16(Nov):2273−2328.\n\n[2] Zhang, D. (2016). Concave Penalized Estimation of Causal Gaussian Networks with Intervention. Master’s thesis, UCLA.\n\n[3] Fu, F. and Zhou, Q. (2013). [Learning sparse causal Gaussian networks with experimental intervention: Regularization and coordinate descent.](http://amstat.tandfonline.com/doi/abs/10.1080/01621459.2012.754359) Journal of the American Statistical Association, 108: 288-300.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fitsrainingdata%2Fccdralgorithm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fitsrainingdata%2Fccdralgorithm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fitsrainingdata%2Fccdralgorithm/lists"}