https://github.com/itsrainingdata/sparsebn

Software for learning sparse Bayesian networks
https://github.com/itsrainingdata/sparsebn

bayesian-networks covariance-matrices experimental-data graphical-models machine-learning r regularization statistics

Last synced: 3 months ago
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Software for learning sparse Bayesian networks

• Host: GitHub
• URL: https://github.com/itsrainingdata/sparsebn
• Owner: itsrainingdata
• Created: 2016-04-05T22:39:35.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2020-09-05T19:14:44.000Z (over 4 years ago)
• Last Synced: 2024-10-15T21:45:18.370Z (3 months ago)
• Topics: bayesian-networks, covariance-matrices, experimental-data, graphical-models, machine-learning, r, regularization, statistics
• Language: R
• Size: 2.84 MB
• Stars: 43
• Watchers: 6
• Forks: 8
• Open Issues: 4
• Metadata Files:
  • Readme: README.Rmd

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README

        
---

output:

  md_document:

    variant: markdown_github

---

```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

```

# sparsebn

[![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/sparsebn.svg?branch=master)](https://travis-ci.org/itsrainingdata/sparsebn)

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

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

Introducing `sparsebn`: A new R package for learning sparse Bayesian networks and other graphical models from high-dimensional data via sparse regularization. Designed from the ground up to handle:

- Experimental data with interventions 

- Mixed observational / experimental data

- High-dimensional data with _p >> n_

- Datasets with thousands of variables (tested up to _p_=8000)

- Continuous and discrete data

The emphasis of this package is scalability and statistical consistency on high-dimensional datasets. Compared to existing algorithms, `sparsebn` scales much better and is under active development. For more details on this package, including worked examples and the methodological background, please see [our new preprint](https://arxiv.org/abs/1703.04025) [[1](#references)].

## Overview

The main methods for learning graphical models are:

* `estimate.dag` for directed acyclic graphs (Bayesian networks).

* `estimate.precision` for undirected graphs (Markov random fields).

* `estimate.covariance` for covariance matrices.

Currently, estimation of precision and covariances matrices is limited to Gaussian data.

The workhorse behind [`sparsebn`](http://www.github.com/itsrainingdata/sparsebn/) is the [`sparsebnUtils`](http://www.github.com/itsrainingdata/sparsebnUtils/)

package, which provides various S3 classes and methods for representing and manipulating graphs. The basic algorithms are implemented in [`ccdrAlgorithm`](http://www.github.com/itsrainingdata/ccdrAlgorithm/) and [`discretecdAlgorithm`](http://www.github.com/gujyjean/discretecdAlgorithm/).

## Installation

You can install:

* the latest CRAN version with

    ```R

    install.packages("sparsebn")

    ````

* the latest development version from GitHub with

    ```R

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

    ```

## References

[1] Aragam, B., Gu, J., and Zhou, Q. (2017). [Learning large-scale Bayesian networks with the sparsebn package.](https://arxiv.org/abs/1703.04025) arXiv: 1703.04025. 

[2] 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. 

[3] Fu, F., Gu, J., and Zhou, Q. (2014). [Adaptive penalized estimation of directed acyclic graphs from categorical data.](http://arxiv.org/abs/1403.2310) arXiv: 1403.2310. 

[4] Aragam, B., Amini, A. A., and Zhou, Q. (2015). [Learning directed acyclic graphs with penalized neighbourhood regression.](http://arxiv.org/abs/1511.08963) arXiv: 1511.08963.

[5] 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.