person("Bernhard", "Pfaff", email = "bernhard@pfaffikus.de", role = c("aut", "cre"))
R (>= 3.1.3), methods, cccp, Rglpk, timeSeries
Accompanying package of the book 'Financial Risk Modelling
and Portfolio Optimisation with R', second edition. The data sets used in the book are contained in this package.

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

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

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

Fast Regularized Canonical Correlation Analysis
This package implements the functions associated with Fast
Regularized Canonical Correlation Analysis.

person("Raul", "Cruz-Cano", , "raulcruz@iu.edu", role = c("aut", "cre"))
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,
Volume 70, 2014, Pages 88-100,
ISSN 0167-9473 <doi:10.1016/j.csda.2013.09.020>.
calibrate, CCP, corpcor, graphics, MASS, stats, utils

Tree Method for High Dimensional Longitudinal Data
c(
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")))
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
while improving computational efficiency.
Yuancheng Xu [aut],
Athanasse Zafirov [cre],
Christina Ramirez [aut],
Dan Kojis [aut],
Min Tan [aut],
Mike Alvarez [aut]