updates

README.Rmd

@@ -12,7 +12,7 @@ output: github_document
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-The package **JMbayes2** fits joint models for longitudinal and time-to-event data. It can accommodate multiple longitudinal outcomes of different type (e.g., continuous, dichotomous, ordinal, counts), and assuming different distributions, i.e., Gaussian, Student's-t, Gamma, Beta, unit Lindley, censored Normal, Binomial, Poisson, Negative Binomial, and Beta-Binomial. For the event time process, right, left and interval censored data can be handled, while competing risks and multi-sate processes are also covered.
+The package **JMbayes2** fits joint models for longitudinal and time-to-event data. It can accommodate multiple longitudinal outcomes of different type (e.g., continuous, dichotomous, ordinal, counts), and assuming different distributions, i.e., Gaussian, Student's-t, Gamma, Beta, unit Lindley, censored Normal, Binomial, Poisson, Negative Binomial, and Beta-Binomial. For the event time process, right, left and interval censored data can be handled, while competing risks, multi-sate and recurrent-event processes are also covered.
 
 **JMbayes2** fits joint models using Markov chain Monte Carlo algorithms implemented in C++. Besides the main modeling function, the package also provides a number of functions to summarize and visualize the results.
 

README.md

@@ -17,8 +17,8 @@ different type (e.g., continuous, dichotomous, ordinal, counts), and
 assuming different distributions, i.e., Gaussian, Student’s-t, Gamma,
 Beta, unit Lindley, censored Normal, Binomial, Poisson, Negative
 Binomial, and Beta-Binomial. For the event time process, right, left and
-interval censored data can be handled, while competing risks and
-multi-sate processes are also covered.
+interval censored data can be handled, while competing risks, multi-sate 
+and recurrent-event processes are also covered..
 
 **JMbayes2** fits joint models using Markov chain Monte Carlo algorithms
 implemented in C++. Besides the main modeling function, the package also