Solve issue with axis labels in ggs_crosscorrelation()
Include data (and samples from models) to facilitate examples.

  - MCMC output from a linear regression model with dummy data
  (data/samples.rda).

  - MCMC output from a logit regression model with dummy data
  (data/samples_binary.rda).

  - MCMC output and other description variables for a hierarchical / multilevel
  model based on radon example at Gelman & Hill.
Major revision of the code moving plyr to dplyr and reshape2 to tidyr.

  - The main object from the ggs() call is no more a data.frame, but a tbl_df().

  - All ddply calls transformed into combinations of group_by() calls. Other
  functions (like cbind(), subset() and rbind() have also been implemented using
  the dplyr style).

  - All melt() and dcast() transformed into gather() and spread() (and even some
  as.data.frame.table()).

  - Deleting the possibility of parallelization. It is not implemented in dplyr,
  althoug the C++ backend that does the real work is clearly faster than the old
  plyr.

  - Major revision of ggmcmc() with a bit of an ad-hoc approach to passing
  attributes when there are more parameters than parameters allowed in each
  page.
Minor change in the comments plyr->dplyr
Homogenize function names, using underscores "_" everywhere, instead of dots ".".

  - Affects calc_bin(), gl_unq() and roc_calc().
Change dependencies from the help.R file (dplyr for plyr and tidyr for reshape2
Improve the documentation of internal functions.
Change the documentation to reflect the changes in the datasets provided by the package.

  - From "data(samples)" to "data(linear)".

  - Improve the examples of ggs_separation() and ggs_rocplot().
Improve the example and specify that the returned object is a data frame tbl.
Reorganize the datasets packed with the package.

  - Increase the data that passes so that several examples can be used
  (including posterior predictive values, posterior expected binary outcomes).

  - Separate them into different objects for the linear and binary examples.
Revise the functions and objects that are passed to globals.R due to latest changes.
Separate the function to calculate credible interval from ggs_caterpillar.

  - Provide a function ci() that calculates credible intervals, for general use
  in ggs() objects, beyond ggs_caterpillar().

  - Adapt ggs_caterpillar() to use the newly created function.
Revise the names of elements in globals.R to accomodate for the changes in ci().
Allow ggs_caterpillar() to work with facet_() by passing variables through par_labels.

By allowing ggs() to keep variables passed through the par_labels argument, it
is possible now to use those variables in ggs_caterpillar(), because the ci()
function is able to join them all and keeps them in the final processed
object.
Bring back again previous commit that allowed to keep par_labels variables.

  - The previous version included a complete move of one part of the code, which
  in fact produces more problems that it solves.

  - So the move is reverted back and the problem with attributes is solved by
  setting them again.
Add the option to simplify traceplots.

Include an argument in ggs_traceplot() in order to simplify the traceplot so
that the printing is not so resource consuming. This also passes to ggmcmc().
Keep the old Parameter name when passing par_labels in ggs().
Keep old parameter name in par_label, the globals.R addition.
Solve problem when joining data frames in ci() with multiple par_labels.
Modified version of radon example.