minor cleaning of DESCRIPTION and README

DESCRIPTION

@@ -48,8 +48,8 @@ Description: Fit Bayesian generalized (non-)linear multilevel models
     response distribution can be predicted in order to perform distributional 
     regression. Prior specifications are flexible and explicitly encourage 
     users to apply prior distributions that actually reflect their beliefs.
-    In addition, model fit can easily be assessed and compared with
-    posterior predictive checks and leave-one-out cross-validation.
+    Model fit can easily be assessed and compared with posterior predictive 
+    checks and leave-one-out cross-validation.
 LazyData: true
 NeedsCompilation: no
 License: GPL (>= 3)

README.Rmd

@@ -21,7 +21,7 @@ knitr::opts_chunk$set(
 
 # brms
 
-The **brms** package provides an interface to fit Bayesian generalized (non-)linear multilevel models using Stan, which is a C++ package for performing full Bayesian inference (see http://mc-stan.org/). The formula syntax is very similar to that of the package lme4 to provide a familiar and simple interface for performing regression analyses. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. In addition, model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation.
+The **brms** package provides an interface to fit Bayesian generalized (non-)linear multilevel models using Stan, which is a C++ package for performing full Bayesian inference (see http://mc-stan.org/). The formula syntax is very similar to that of the package lme4 to provide a familiar and simple interface for performing regression analyses. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation.
 
 <!--
 ```{r set-options, echo=FALSE}

README.md

@@ -4,7 +4,7 @@
 brms
 ====
 
-The **brms** package provides an interface to fit Bayesian generalized (non-)linear multilevel models using Stan, which is a C++ package for performing full Bayesian inference (see <http://mc-stan.org/>). The formula syntax is very similar to that of the package lme4 to provide a familiar and simple interface for performing regression analyses. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. In addition, model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation.
+The **brms** package provides an interface to fit Bayesian generalized (non-)linear multilevel models using Stan, which is a C++ package for performing full Bayesian inference (see <http://mc-stan.org/>). The formula syntax is very similar to that of the package lme4 to provide a familiar and simple interface for performing regression analyses. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation.
 
 <!--
 
@@ -116,12 +116,13 @@ methods(class = "brmsfit")
 #> [25] ngrps                   nobs                    nsamples               
 #> [28] nuts_params             pairs                   parnames               
 #> [31] plot                    posterior_predict       posterior_samples      
-#> [34] pp_check                predict                 predictive_error       
-#> [37] print                   prior_samples           prior_summary          
-#> [40] ranef                   residuals               rhat                   
-#> [43] stancode                standata                stanplot               
-#> [46] summary                 update                  VarCorr                
-#> [49] vcov                    waic                    WAIC                   
+#> [34] pp_check                pp_mixture              predict                
+#> [37] predictive_error        print                   prior_samples          
+#> [40] prior_summary           ranef                   residuals              
+#> [43] rhat                    stancode                standata               
+#> [46] stanplot                summary                 update                 
+#> [49] VarCorr                 vcov                    waic                   
+#> [52] WAIC                   
 #> see '?methods' for accessing help and source code
 ```