stockdata

<p>This data set consists of stock price and company information.</p>

datasets

Provides a general framework for
high-dimensional undirected graph estimation. It integrates
data preprocessing, neighborhood screening, graph estimation,
and model selection techniques into a pipeline. In
preprocessing stage, the nonparanormal(npn) transformation is
applied to help relax the normality assumption. In the graph
estimation stage, the graph structure is estimated by
Meinshausen-Buhlmann graph estimation or the graphical lasso,
and both methods can be further accelerated by the lossy
screening rule preselecting the neighborhood of each variable
by correlation thresholding. We target on high-dimensional data
analysis usually d >> n, and the computation is
memory-optimized using the sparse matrix output. We also
provide a computationally efficient approach, correlation
thresholding graph estimation. Three
regularization/thresholding parameter selection methods are
included in this package: (1)stability approach for
regularization selection (2) rotation information criterion (3)
extended Bayesian information criterion which is only available
for the graphical lasso.

Haoming Jiang

huge

High-Dimensional Undirected Graph Estimation

stockdata function

<p>The format is a list containing contains two matrices.
1. data - 1258x452, represents the 452 stocks' close prices for 1258 trading days.
  2. info - 452x3:
  The 1st column: the query symbol for each company.
  The 2nd column: the category for each company.
  The 3rd column: the full name of each company.</p>

stockdata: Stock price of S&P 500 companies from 2003 to 2008

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