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README.Rmd
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README.Rmd
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---
output:
md_document:
variant: markdown_github
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
# sparsebn
[](http://www.repostatus.org/#active)
[](https://travis-ci.org/itsrainingdata/sparsebn)
[](http://www.r-pkg.org/pkg/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.