Update README

Author: itsrainingdata

README.Rmd

@@ -19,17 +19,13 @@ knitr::opts_chunk$set(
 [![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 \[[1](#references)\]. Based on observational data, this algorithm estimates the structure of a Bayesian network (aka edges in a DAG) using penalized maximum likelihood based on L1 or concave (MCP) regularization.
+`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 consists of a single method that implements the main algorithm; more functionality will be provided in the future. To generate data from a given Bayesian network and/or simulate random networks, the following R packages are recommended:
-
-- `bnlearn`: [bnlearn on CRAN](https://cran.r-project.org/package=bnlearn), [www.bnlearn.com](http://www.bnlearn.com)
-- `pcalg`: [pcalg on CRAN](https://cran.r-project.org/package=pcalg)
-- `igraph`: [igraph on CRAN](https://cran.r-project.org/package=igraph), [http://igraph.org/r/](http://igraph.org/r/)
+Presently, this package consists of a methods that implement the main algorithm and generate data (with interventions if necessary) 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 \[[1](#references)\].
+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
 
@@ -49,6 +45,8 @@ You can install:
 
 ## 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. 
+\[1\] 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.
 
-\[2\] 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.
+\[3\] 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.

README.md

@@ -3,18 +3,16 @@ ccdrAlgorithm
 
 [![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 \[[1](#references)\]. Based on observational data, this algorithm estimates the structure of a Bayesian network (aka edges in a DAG) using penalized maximum likelihood based on L1 or concave (MCP) regularization.
+`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 consists of a single method that implements the main algorithm; more functionality will be provided in the future. To generate data from a given Bayesian network and/or simulate random networks, the following R packages are recommended:
-
--   `bnlearn`: [bnlearn on CRAN](https://cran.r-project.org/package=bnlearn), [www.bnlearn.com](http://www.bnlearn.com)
--   `pcalg`: [pcalg on CRAN](https://cran.r-project.org/package=pcalg)
--   `igraph`: [igraph on CRAN](https://cran.r-project.org/package=igraph), <http://igraph.org/r/>
+Presently, this package consists of a methods that implement the main algorithm and generate data (with interventions if necessary) 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 \[[1](#references)\].
+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
 ------------
@@ -36,6 +34,11 @@ You can install:
 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.
+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.
 
-\[2\] 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.
+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.