stockdata

This data set consists of stock price and company information.

datasets

The package "huge" 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.

Tuo Zhao  Han Liu hanliucsjhuedu

huge

High-dimensional Undirected Graph Estimation

stockdata function

The format is a list containing conatins 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 categoriy for each company.
  The 3rd column: the full name of each company.

format

It is publicly available at http://ichart.finance.yahoo.com

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

Description

Usage

Arguments

format

source

Details

References

Examples