Robust Regularized Singular Value Decomposition
This package provides the function to calculate SVD, regularized SVD, robust SVD and robust regularized SVD method. The robust SVD methods use alternating iteratively reweighted least squares methods. The regularized SVD uses generalized cross validation to choose the optimal smoothing parameters.

Optimally robust influence curves for regression and scale
functions for the determination of optimally
robust influence curves in case of linear regression
with unknown scale and standard normal distributed errors
where the regressor is random.

functions for the determination of optimally robust
influence curves in case of linear regression with unknown
scale and standard normal distributed errors where the
regressor is random.

Functions for the determination of optimally robust influence curves in case of
linear regression with unknown scale and standard normal distributed errors where the
regressor is random.

Optimally Robust Influence Curves for Regression and Scale

Functions for the determination of optimally robust influence curves in case of
linear regression with unknown scale and standard normal distributed errors where
the regressor is random.

c(person("Matias", "Salibian-Barrera", role = c("cre"), email = "matias@stat.ubc.ca"),
person("Victor", "Yohai", role = "aut", email = "vyohai@gmail.com"),
person("Ricardo", "Maronna", role="aut", email= "rmaronna@retina.ar"),
person("Doug", "Martin", role="aut", email="martinrd3d@gmail.com"),
person("Gregory", "Brownson", role="aut", email="gregory.brownson@gmail.com", comment="ShinyUI"),
person("Kjell", "Konis", role="aut", email="kjellk@gmail.com"),
person("Kjell", "Konis", role="cph", email="kjellk@gmail.com", comment="erfi"),
person("Christophe", "Croux", role="ctb", email="christophe.croux@edhec.edu", comment="WBYlogreg, BYlogreg"),
person("Gentiane", "Haesbroeck", role="ctb", email="G.Haesbroeck@uliege.be", comment="WBYlogreg, BYlogreg"),
person("Martin", "Maechler", role="cph", email="maechler@stat.math.ethz.ch", comment="lmrob.fit, lmrob..M..fit, lmrob.S"),
person("Manuel", "Koller", role="cph", email="koller.manuel@gmail.com", comment="lmrob.fit, .vcov.avar1, lmrob.S, lmrob.lar"),
person("Matias", "Salibian-Barrera", role="aut", email="matias@stat.ubc.ca")
)
Companion package for the book: "Robust Statistics: Theory and Methods, second edition", <http://www.wiley.com/go/maronna/robust>.  This package contains code that implements the robust estimators discussed in the recent second edition of the book above, as well as the scripts reproducing all the examples in the book.
stats, graphics, utils, methods, DEoptimR, pyinit, rrcov,
robustbase, shiny, shinyjs, PerformanceAnalytics, DT, ggplot2,
gridExtra, robust, xts
Matias Salibian-Barrera [cre],
Victor Yohai [aut],
Ricardo Maronna [aut],
Doug Martin [aut],
Gregory Brownson [aut] (ShinyUI),
Kjell Konis [aut],
Kjell Konis [cph] (erfi),
Christophe Croux [ctb] (WBYlogreg, BYlogreg),
Gentiane Haesbroeck [ctb] (WBYlogreg, BYlogreg),
Martin Maechler [cph] (lmrob.fit, lmrob..M..fit, lmrob.S),
Manuel Koller [cph] (lmrob.fit, .vcov.avar1, lmrob.S, lmrob.lar),
Matias Salibian-Barrera [aut]