China_Seas

<p>A dataset with the distributions of some climatic variables collected for each season in 60 stations of China.
The collected variables are 56 i.e. 14 climatic variables observed for 4 seasons. The 14 variables are the following:
 mean station pressure (mb), mean temperature, mean maximum temperature, mean minimum temperature,
  total precipitation (mm), sunshine duration (h), mean cloud amount (percentage of sky cover),
  mean relative humidity (
   mean wind speed (m/s), dominant wind frequency (
   extreme minimum temperature. 
Use the command <code> get.MatH.main.info(China_Seas)</code> for rapid info.</p>

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>.

China_Seas: A seasonal climatic dataset of China

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