Fix merge conflict

Merge branch 'master' of https://github.com/itsrainingdata/ccdrAlgorithm

# Conflicts:
#	README.Rmd
#	README.md

Author: itsrainingdata

README.Rmd

@@ -18,7 +18,6 @@ knitr::opts_chunk$set(
 [![](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 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).
@@ -45,8 +44,8 @@ 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] 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.
+[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

@@ -34,11 +34,8 @@ 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.
+\[2\] 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.
+\[3\] 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.