Imports: reshape2, leaps, vcd, colorspace, RColorBrewer, shiny (>=
0.13.1), Hmisc, abind, Rmpfr (>= 0.6.0), grDevices, methods
Suggests: mvtnorm, car, Rcmdr, RcmdrPlugin.HH, TeachingDemos

Suggests: mvtnorm, car, Rcmdr, RcmdrPlugin.HH, TeachingDemos, microplot

Imports: reshape2, leaps, vcd, colorspace, RColorBrewer, Rcmdr
Description: Support software for Statistical Analysis and Data Display (First Edition, Springer, ISBN 0-387-40270-5, 2004) and (Second Edition, Springer, ISBN x-xxx-xxxxx-x, anticipated 2015) by Richard M. Heiberger and Burt Holland.  This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis.  The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print.  All chapters have exercises.  All functions introduced in the book are in the package.  R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.

Title: Heller-Heller-Gorfine Tests of Independence
Author: Shachar Kaufman, based in part on an earlier implementation by Ruth Heller and Yair Heller.
Description: Heller-Heller-Gorfine (HHG) tests are a set of statistical tests of independence between two random vectors of arbitrary dimensions. For the case of testing independence between random variables (rather than vectors), the package also offers implementations of the DDP and ADP tests which are consistent against all continuous alternatives but are distribution-free, and thus much faster to apply.

Description: Heller-Heller-Gorfine (HHG) tests are a set of powerful statistical tests of independence between two random vectors of arbitrary dimensions. For the case of testing independence between random variables (rather than vectors), the package also offers implementations of the DDP and ADP tests, which are consistent against all continuous alternatives but are distribution-free and thus much faster to apply.