rstanarm-package.R: add stan logo

Author: jgabry

R/rstanarm-package.R

@@ -33,29 +33,35 @@
 #' @export loo waic compare
 #' @export launch_shinystan
 #' 
-#' @description An appendage to the \pkg{rstan} package that enables some of the
-#'   most common applied regression models to be estimated using Markov Chain 
-#'   Monte Carlo, variational approximations to the posterior distribution, or 
-#'   optimization. The \pkg{rstanarm} package allows these models to be 
-#'   specified using the customary R modeling syntax (e.g., like that of 
-#'   \code{\link[stats]{glm}} with a \code{formula} and a \code{data.frame}).
-#'   
-#'   The set of models supported by \pkg{rstanarm} is large (and will continue 
-#'   to grow), but also limited enough so that it is possible to integrate them 
-#'   tightly with the \code{\link{pp_check}} function for graphical posterior
-#'   predictive checks and the \code{\link{posterior_predict}} function to
-#'   easily estimate the effect of specific manipulations of predictor variables
-#'   or to predict the outcome in a training set.
-#'   
-#'   The objects returned by the \pkg{rstanarm} modeling functions are called
-#'   \code{\link[=stanreg-objects]{stanreg}} objects. In addition to all of the
-#'   typical \code{\link[=stanreg-methods]{methods}} defined for fitted model
-#'   objects, stanreg objects can be passed to the \code{\link[loo]{loo}}
-#'   function in the \pkg{loo} package for model comparison or to the
-#'   \code{\link[shinystan]{launch_shinystan}} function in the \pkg{shinystan}
-#'   package in order to visualize the posterior distribution using the
-#'   ShinyStan graphical user interface. See the \pkg{rstanarm} vignettes for
-#'   more details about the entire process.
+#' @description 
+#' \if{html}{
+#'    \figure{stanlogo.png}{options: width="50px" alt="mc-stan.org"}
+#'    \emph{Stan Development Team}
+#' }
+#' 
+#' An appendage to the \pkg{rstan} package that enables some of the most common
+#' applied regression models to be estimated using Markov Chain Monte Carlo,
+#' variational approximations to the posterior distribution, or optimization.
+#' The \pkg{rstanarm} package allows these models to be specified using the
+#' customary R modeling syntax (e.g., like that of \code{\link[stats]{glm}} with
+#' a \code{formula} and a \code{data.frame}).
+#' 
+#' The set of models supported by \pkg{rstanarm} is large (and will continue to
+#' grow), but also limited enough so that it is possible to integrate them 
+#' tightly with the \code{\link{pp_check}} function for graphical posterior 
+#' predictive checks and the \code{\link{posterior_predict}} function to easily
+#' estimate the effect of specific manipulations of predictor variables or to
+#' predict the outcome in a training set.
+#' 
+#' The objects returned by the \pkg{rstanarm} modeling functions are called 
+#' \code{\link[=stanreg-objects]{stanreg}} objects. In addition to all of the 
+#' typical \code{\link[=stanreg-methods]{methods}} defined for fitted model 
+#' objects, stanreg objects can be passed to the \code{\link[loo]{loo}} function
+#' in the \pkg{loo} package for model comparison or to the 
+#' \code{\link[shinystan]{launch_shinystan}} function in the \pkg{shinystan} 
+#' package in order to visualize the posterior distribution using the ShinyStan
+#' graphical user interface. See the \pkg{rstanarm} vignettes for more details
+#' about the entire process.
 #'
 #' @section Estimation algorithms: 
 #' The modeling functions in the \pkg{rstanarm} package take an \code{algorithm}