Fixed README, added version number for sparsebnUtils

Author: itsrainingdata

DESCRIPTION

@@ -8,7 +8,7 @@ Description: Implementation of the CCDr (Concave penalized Coordinate Descent wi
 Depends:
     R (>= 3.2.3)
 Imports:
-    sparsebnUtils,
+    sparsebnUtils (>= 0.0.2),
     Rcpp (>= 0.11.0)
 LinkingTo: Rcpp
 Suggests:

README.Rmd

@@ -16,7 +16,7 @@ knitr::opts_chunk$set(
 
 [![Travis-CI Build Status](https://travis-ci.org/itsrainingdata/ccdrAlgorithm.svg?branch=master)](https://travis-ci.org/itsrainingdata/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 \[[1](#references)\]. 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.
 
 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:
 

README.md

@@ -3,7 +3,7 @@ ccdrAlgorithm
 
 [![Travis-CI Build Status](https://travis-ci.org/itsrainingdata/ccdrAlgorithm.svg?branch=master)](https://travis-ci.org/itsrainingdata/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 \[[1](#references)\]. 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.
 
 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: