Revise package description.

README.md

@@ -9,7 +9,7 @@
 
 ## Purpose
 
-*Mamba* is a [julia](http://julialang.org/) programming environment and toolset for the implementation and inference of Bayesian models using MCMC sampling.  The package provides a framework for (1) specification of hierarchical models through stated relationships between data, parameters, and statistical distributions; (2) block-updating of parameters with samplers provided, defined by the user, or available from other packages; (3) execution of sampling schemes; and (4) posterior inference.  It is designed to give users access to all levels of the design and implementation of MCMC simulators to particularly aid in the development of complex models.
+*Mamba* is an open platform for the implementation and application of MCMC methods to perform Bayesian analysis in [julia](http://julialang.org/).  The package provides a framework for (1) specification of hierarchical models through stated relationships between data, parameters, and statistical distributions; (2) block-updating of parameters with samplers provided, defined by the user, or available from other packages; (3) execution of sampling schemes; and (4) posterior inference.  It is intended to give users access to all levels of the design and implementation of MCMC simulators to particularly aid in the development of new methods.
 
 Several software options are available for MCMC sampling of Bayesian models.  Individuals who are primarily interested in data analysis, unconcerned with the details of MCMC, and have models that can be fit in [JAGS](http://mcmc-jags.sourceforge.net/), [Stan](http://mc-stan.org/), or [OpenBUGS](http://www.openbugs.net/) are encouraged to use those programs.  *Mamba* is intended for individuals who wish to have access to lower-level MCMC tools, are knowledgeable of MCMC methodologies, and have experience, or wish to gain experience, with their application.  The package also provides stand-alone convergence diagnostics and posterior inference tools, which are essential for the analysis of MCMC output regardless of the software used to generate it. 
 

doc/index.rst

@@ -15,7 +15,7 @@ Overview
 Purpose
 ^^^^^^^
 
-`Mamba` is a `julia <http://julialang.org/>`_ programming environment and toolset for the implementation and inference of Bayesian models using MCMC sampling.  The package provides a framework for (1) specification of hierarchical models through stated relationships between data, parameters, and statistical distributions; (2) block-updating of parameters with samplers provided, defined by the user, or available from other packages; (3) execution of sampling schemes; and (4) posterior inference.  It is designed to give users access to all levels of the design and implementation of MCMC simulators to particularly aid in the development of complex models.
+`Mamba` is an open platform for the implementation and application of MCMC methods to perform Bayesian analysis in `julia <http://julialang.org/>`_.  The package provides a framework for (1) specification of hierarchical models through stated relationships between data, parameters, and statistical distributions; (2) block-updating of parameters with samplers provided, defined by the user, or available from other packages; (3) execution of sampling schemes; and (4) posterior inference.  It is intended to give users access to all levels of the design and implementation of MCMC simulators to particularly aid in the development of new methods.
 
 Several software options are available for MCMC sampling of Bayesian models.  Individuals who are primarily interested in data analysis, unconcerned with the details of MCMC, and have models that can be fit in `JAGS <http://mcmc-jags.sourceforge.net/>`_, `Stan <http://mc-stan.org/>`_, or `OpenBUGS <http://www.openbugs.net/>`_ are encouraged to use those programs.  `Mamba` is intended for individuals who wish to have access to lower-level MCMC tools, are knowledgeable of MCMC methodologies, and have experience, or wish to gain experience, with their application.  The package also provides stand-alone convergence diagnostics and posterior inference :ref:`tools <section-Chains>`, which are essential for the analysis of MCMC output regardless of the software used to generate it.