Author: Ella Roelant, Stefan Van Aelst, Gert Willems
Description: This package performs robust inference based on applying
Fast and Robust Bootstrap on robust estimators. Available
methods are multivariate regression, PCA and Hotelling tests.

Maintainer: Stefan Van Aelst <Stefan.VanAelst@ugent.be>

Title: Download financial data from FRB's website
Description: This package provides functions which can get financial
Packaged: 2011-04-19 10:15:54 UTC; Administrator

Packaged: 2011-04-24 06:53:13 UTC; Administrator

Title: Download interest rate data from FRB's website
Description: This package provides functions which can get financial and economical data from FRB's website.(http://www.federalreserve.gov)

Title: Fast Regularized Canonical Correlation Analysis
Description: This package implements the functions associated with Fast

person("Raul", "Cruz-Cano", , "raulcruz@iu.edu", role = c("aut", "cre"))
Description: Contains the core functions associated with Fast
Regularized Canonical Correlation Analysis. Please see the following for
details: Raul Cruz-Cano, Mei-Ling Ting Lee,
Fast regularized canonical correlation analysis,
Computational Statistics & Data Analysis,
Imports: calibrate, CCP, corpcor, graphics, MASS, stats, utils
Packaged: 2022-10-28 14:01:27 UTC; aaroncohen

Title: Tree Method for High Dimensional Longitudinal Data
person("Yuancheng", "Xu", email = "xuyuancheng0@gmail.com", role = c("aut")),
person("Athanasse", "Zafirov", email = "zafirov@gmail.com", role = c("cre")),
person("Christina", "Ramirez", email = "cr@ucla.edu", role = c("aut")),
person("Dan", "Kojis", email = "x@xxx.edu", role = c("aut")),
person("Min", "Tan", email = "xx@xxx.edu", role = c("aut")),
person("Mike", "Alvarez", email = "rma@hss.caltech.edu", role = c("aut")))
Description: This tree-based method deals with high dimensional longitudinal
data with correlated features through the use of a piecewise random effect
model. FREE tree also exploits the network structure of the features, by
first clustering them using Weighted Gene Co-expression Network Analysis
('WGCNA'). It then conducts a screening step within each cluster of features
and a selecting step among the surviving features, which provides a relatively
unbiased way to do feature selection. By using dominant principle components
as regression variables at each leaf and the original features as splitting
variables at splitting nodes, FREE tree delivers easily interpretable results