Title: COSSO, adaptive COSSO, variable selection for nonparametric
Author: Hao Helen Zhang <hzhang@stat.ncsu.edu>
Maintainer: Hao Helen Zhang <hzhang@stat.ncsu.edu> and Chen-Yen Lin
Description: COSSO is the short term for Component Selection and
Smoothing Operator. It is a new regularization method for
multivariate function estimation and component. The COSSO
imposes a soft-thresholding penalty operator on function
components in the framework of reproducing kernel Hilbert space
(RKHS) to achieve sparsity. In a sense, the COSSO generalizes
the LASSO (Tibshirani 1996) for variable selection in linear
models to nonparametric regression models. Details about the
procedures are given in the papers by Lin and Zhang (2006) and

Author: Hao Helen Zhang <hzhang@stat.ncsu.edu> and Chen-Yen Lin
Smoothing Operator. The new regularization method automatically
selects important function components by a soft-thresholding
penalty in the context of smoothing spline ANOVA models.  The
details about the procedures are given in the paper by Lin and
Depends: quadprog, Rglpk, snow, snowfall

smoothing spline (quantile) regression models.

Title: Fit Regularized Nonparametric Regression Models Using COSSO
Description: COSSO is the short term for COmponent Selection and
estimates and selects important function components by a
soft-thresholding penalty in the context of smoothing spline
Depends: quadprog, Rglpk, snow, snowfall, glmnet

Author: Hao Helen Zhang <hzhang@math.arizona.edu> and Chen-Yen Lin
Description: COSSO is a new regularization method that automatically
ANOVA models. Implemented models include mean regression,
quantile regression, logistic regression and the Cox regression
Depends: quadprog, Rglpk, parallel, glmnet

Title: Time series costationarity determination and tests of
Author: Guy Nason and Alessandro Cardinali
Description: Contains functions that can determine whether a time
series is second-order stationary or not (and hence evidence
for locally stationarity). Given two non-stationary series
(i.e. locally stationary series) this package can then discover
time-varying linear combinations that are second-order