https://github.com/itsrainingdata/ccdralgorithm

Structure learning for Bayesian networks using the CCDr algorithm.
https://github.com/itsrainingdata/ccdralgorithm

bayesian-networks experimental-data graphical-models machine-learning-algorithms r regularization statistical-learning

Last synced: 10 months ago
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Structure learning for Bayesian networks using the CCDr algorithm.

• Host: GitHub
• URL: https://github.com/itsrainingdata/ccdralgorithm
• Owner: itsrainingdata
• Created: 2016-03-11T02:45:58.000Z (almost 10 years ago)
• Default Branch: master
• Last Pushed: 2024-01-10T09:00:43.000Z (about 2 years ago)
• Last Synced: 2025-03-27T03:51:12.638Z (10 months ago)
• Topics: bayesian-networks, experimental-data, graphical-models, machine-learning-algorithms, r, regularization, statistical-learning
• Language: C++
• Homepage:
• Size: 298 KB
• Stars: 6
• Watchers: 3
• Forks: 9
• Open Issues: 1
• Metadata Files:
  • Readme: README.Rmd

Awesome Lists containing this project

README

          
---

output:

  md_document:

    variant: markdown_github

---

```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

```

# ccdrAlgorithm

[![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)

[![Travis-CI Build Status](https://travis-ci.org/itsrainingdata/ccdrAlgorithm.svg?branch=master)](https://travis-ci.org/itsrainingdata/ccdrAlgorithm)

[![](http://www.r-pkg.org/badges/version/ccdrAlgorithm)](http://www.r-pkg.org/pkg/ccdrAlgorithm)

[![CRAN RStudio mirror downloads](http://cranlogs.r-pkg.org/badges/ccdrAlgorithm)](http://www.r-pkg.org/pkg/ccdrAlgorithm)

`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.

Presently, 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).

## Overview

The 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).

## Installation

You can install:

* the latest CRAN version with

    ```R

    install.packages("ccdrAlgorithm")

    ````

* the latest development version from GitHub with

    ```R

    devtools::install_github(c("itsrainingdata/sparsebnUtils/dev", "itsrainingdata/ccdrAlgorithm/dev"))

    ```

## References

[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.

[2] Zhang, D. (2016). Concave Penalized Estimation of Causal Gaussian Networks with Intervention. Master’s thesis, UCLA.

[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.