Package: cvplogistic
Version: 3.1-0
Date: 2013-03-17
Title: Penalized Logistic Regression Model using Majorization
        Minimization by Coordinate Descent (MMCD) Algorithm
Author: Dingfeng Jiang <dingfengjiang@gmail.com>
Maintainer: Dingfeng Jiang <dingfengjiang@gmail.com>
Depends: R (>= 2.9.0)
Description: The package uses majorization minimization by coordinate
        descent (MMCD) algorithm to compute the solution surface for
        concave penalized logistic regression model.  The SCAD and MCP
        (default) are two concave penalties considered in this
        implementation.  For the MCP penalty, the package also provides
        the local linear approximation by coordinate descant (LLA-CD)
        and adaptive rescaling algorithms for computing the solutions.
        The package also provides a Lasso-concave hybrid penalty for
        fast variable selection. The hybrid penalty applies the concave
        penalty only to the variables selected by the Lasso.  For all
        the implemented methods, the solution surface is computed along
        kappa, which is a more smooth fit for the logistic model.
        Tuning parameter selection method by k-fold cross-validated
        area under ROC curve (CV-AUC) is implemented as well.
License: GPL (>= 2)
URL: http://www.r-project.org
Packaged: 2013-03-18 02:04:48 UTC; luwenjing
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2013-03-18 10:29:02