The 'HistData' package provides a collection of small data sets
    that are interesting and important in the history of statistics and data
    visualization. The goal of the package is to make these available, both for
    instructional use and for historical research. Some of these present interesting
    challenges for graphics or analysis in R.

In the framework of Symbolic Data Analysis, a relatively new
    approach to the statistical analysis of multi-valued data, we consider
    histogram-valued data, i.e., data described by univariate histograms. The
    methods and the basic statistics for histogram-valued data are mainly based
    on the L2 Wasserstein metric between distributions, i.e., the Euclidean metric
    between quantile functions. The package contains unsupervised classification
    techniques, least square regression and tools for histogram-valued data and for
    histogram time series. An introducing paper is Irpino A. Verde R. (2015) <doi: 10.1007/s11634-014-0176-4>.

Using the MDL principle, it is possible to estimate
	parameters for a histogram-like model. The package contains
	the implementation of such an estimation method.

Automatic construction of regular and irregular histograms as described in Rozenholc/Mildenberger/Gather (2010).

Historical borrowing in clinical trials can improve
  precision and operating characteristics. This package supports
  a hierarchical model and a mixture model to borrow historical
  control data from other studies to better characterize the
  control response of the current study. It also quantifies
  the amount of borrowing through benchmark models (independent
  and pooled). Some of the methods are discussed by
  Viele et al. (2013) <doi:10.1002/pst.1589>.

Historical borrowing in clinical trials can improve
  precision and operating characteristics. This package supports
  a longitudinal hierarchical model to borrow historical
  control data from other studies to better characterize the
  control response of the current study. It also quantifies
  the amount of borrowing through longitudinal benchmark models (independent
  and pooled). The hierarchical model approach to historical borrowing
  is discussed by Viele et al. (2013) <doi:10.1002/pst.1589>.