README.Rmd

---
output:
  md_document:
    variant: markdown_github
---

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# ccdrAlgorithm

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`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.