diff --git a/README.Rmd b/README.Rmd
index f0e7215..7ad9c04 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -16,13 +16,12 @@ knitr::opts_chunk$set(
 
 [![Travis-CI Build Status](https://travis-ci.org/graemeleehickey/joineR.svg?branch=master)](https://travis-ci.org/graemeleehickey/joineR)
 [![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/github/graemeleehickey/joineR?branch=master&svg=true)](https://ci.appveyor.com/project/graemeleehickey/joineR)
-[![License](https://img.shields.io/badge/License-GPL%20%28%3E=%203%29-brightgreen.svg)](http://www.gnu.org/licenses/gpl-3.0.html)
 [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/joineR)](https://CRAN.R-project.org/package=joineR)
 [![](http://cranlogs.r-pkg.org/badges/joineR)](https://CRAN.R-project.org/package=joineR)
 [![](https://cranlogs.r-pkg.org/badges/grand-total/joineR)](https://CRAN.R-project.org/package=joineR)
-[![Coverage Status](https://img.shields.io/codecov/c/github/graemeleehickey/joineR/master.svg)](https://codecov.io/github/graemeleehickey/joineR?branch=master)
+[![codecov](https://codecov.io/gh/graemeleehickey/joineR/branch/master/graph/badge.svg)](https://codecov.io/gh/graemeleehickey/joineR)
 
-The `joineR` package implements methods for analyzing data from longitudinal studies in which the response from each subject consists of a time-sequence of repeated measurements and a possibly censored time-to- event outcome. The modelling framework for the repeated measurements is the linear model with random effects and/or correlated error structure (Laird and Ware, 1982). The model for the time-to-event outcome is a Cox proportional hazards model with log-Gaussian frailty (Cox, 1972). Stochastic dependence is captured by allowing the Gaussian random effects of the linear model to be correlated with the frailty term of the Cox proportional hazards model. The methodology used to fit the model is described in Henderson et al. (2002) and Wulfsohn and Tsiatis (1997).
+The `joineR` package implements methods for analyzing data from longitudinal studies in which the response from each subject consists of a time-sequence of repeated measurements and a possibly censored time-to-event outcome. The modelling framework for the repeated measurements is the linear model with random effects and/or correlated error structure (Laird and Ware, 1982). The model for the time-to-event outcome is a Cox proportional hazards model with log-Gaussian frailty (Cox, 1972). Stochastic dependence is captured by allowing the Gaussian random effects of the linear model to be correlated with the frailty term of the Cox proportional hazards model. The methodology used to fit the model is described in Henderson et al. (2002) and Wulfsohn and Tsiatis (1997).
 
 The `joineR` package also allows competing risks data to be jointly modelled through a cause-specific hazards model. The importance of accounting for competing risks is detailed in Williamson et al. (2007a,b). The methodology used to fit this model is described in Williamson et al. (2008).
 
diff --git a/README.md b/README.md
index 7e62262..09d57d6 100644
--- a/README.md
+++ b/README.md
@@ -3,9 +3,9 @@
 joineR
 ======
 
-[![Travis-CI Build Status](https://travis-ci.org/graemeleehickey/joineR.svg?branch=master)](https://travis-ci.org/graemeleehickey/joineR) [![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/github/graemeleehickey/joineR?branch=master&svg=true)](https://ci.appveyor.com/project/graemeleehickey/joineR) [![License](https://img.shields.io/badge/License-GPL%20%28%3E=%203%29-brightgreen.svg)](http://www.gnu.org/licenses/gpl-3.0.html) [![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/joineR)](https://CRAN.R-project.org/package=joineR) [![](http://cranlogs.r-pkg.org/badges/joineR)](https://CRAN.R-project.org/package=joineR) [![](https://cranlogs.r-pkg.org/badges/grand-total/joineR)](https://CRAN.R-project.org/package=joineR) [![Coverage Status](https://img.shields.io/codecov/c/github/graemeleehickey/joineR/master.svg)](https://codecov.io/github/graemeleehickey/joineR?branch=master)
+[![Travis-CI Build Status](https://travis-ci.org/graemeleehickey/joineR.svg?branch=master)](https://travis-ci.org/graemeleehickey/joineR) [![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/github/graemeleehickey/joineR?branch=master&svg=true)](https://ci.appveyor.com/project/graemeleehickey/joineR) [![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/joineR)](https://CRAN.R-project.org/package=joineR) [![](http://cranlogs.r-pkg.org/badges/joineR)](https://CRAN.R-project.org/package=joineR) [![](https://cranlogs.r-pkg.org/badges/grand-total/joineR)](https://CRAN.R-project.org/package=joineR) [![codecov](https://codecov.io/gh/graemeleehickey/joineR/branch/master/graph/badge.svg)](https://codecov.io/gh/graemeleehickey/joineR)
 
-The `joineR` package implements methods for analyzing data from longitudinal studies in which the response from each subject consists of a time-sequence of repeated measurements and a possibly censored time-to- event outcome. The modelling framework for the repeated measurements is the linear model with random effects and/or correlated error structure (Laird and Ware, 1982). The model for the time-to-event outcome is a Cox proportional hazards model with log-Gaussian frailty (Cox, 1972). Stochastic dependence is captured by allowing the Gaussian random effects of the linear model to be correlated with the frailty term of the Cox proportional hazards model. The methodology used to fit the model is described in Henderson et al. (2002) and Wulfsohn and Tsiatis (1997).
+The `joineR` package implements methods for analyzing data from longitudinal studies in which the response from each subject consists of a time-sequence of repeated measurements and a possibly censored time-to-event outcome. The modelling framework for the repeated measurements is the linear model with random effects and/or correlated error structure (Laird and Ware, 1982). The model for the time-to-event outcome is a Cox proportional hazards model with log-Gaussian frailty (Cox, 1972). Stochastic dependence is captured by allowing the Gaussian random effects of the linear model to be correlated with the frailty term of the Cox proportional hazards model. The methodology used to fit the model is described in Henderson et al. (2002) and Wulfsohn and Tsiatis (1997).
 
 The `joineR` package also allows competing risks data to be jointly modelled through a cause-specific hazards model. The importance of accounting for competing risks is detailed in Williamson et al. (2007a,b). The methodology used to fit this model is described in Williamson et al. (2008).