c(person(given = "Marius", family = "Hofert", role = c("aut", "cre"),
email = "marius.hofert@uwaterloo.ca"),
person(given = "Kurt", family = "Hornik", role = "aut",
email = "Kurt.Hornik@R-project.org"),
person(given = "Alexander J.", family = "McNeil", role = "aut",
email = "alexander.mcneil@york.ac.uk"))
Marius Hofert [aut, cre],
Kurt Hornik [aut],
Alexander J. McNeil [aut]

Maria de los Angeles Resa, Birol Emir, Javier Cabrera
Maria de los Angeles Resa <maria@stat.columbia.edu>
Implements the robust algorithm for fitting finite mixture models based on quantile regression proposed by Emir et al., 2017 (unpublished).

Functions and data sets for reproducing selected results from
the book "Quantitative Risk Management: Concepts, Techniques and Tools".
Furthermore, new developments and auxiliary functions for Quantitative
Risk Management practice.

graphics, lattice, quantmod, Quandl, zoo, xts, methods,
grDevices, stats, rugarch, utils
combinat, copula, knitr, mvtnorm, QRM, sfsmisc, RColorBrewer,
sn

graphics, lattice, quantmod, Quandl, zoo, xts, methods,
grDevices, stats, rugarch, utils, ADGofTest
combinat, copula, knitr, sfsmisc, RColorBrewer, sn

combinat, copula, qrng, sfsmisc, RColorBrewer, sn, knitr,
rmarkdown

combinat, copula, knitr, mvtnorm, QRM, rugarch, sfsmisc

quantmod, Quandl, zoo, methods, grDevices, stats

graphics, lattice, quantmod, Quandl, zoo, methods, grDevices,
stats, rugarch, utils, xts
combinat, copula, knitr, mvtnorm, QRM, sfsmisc

Functionality for generating (randomized) quasi-random numbers in
high dimensions.
c(person(given = "Marius", family = "Hofert", role = c("aut", "cre"),
email = "marius.hofert@uwaterloo.ca"),
person(given = "Christiane", family = "Lemieux", role = "aut",
email = "clemieux@uwaterloo.ca"))
Marius Hofert [aut, cre], Christiane Lemieux [aut]

Marius Hofert [aut, cre],
Christiane Lemieux [aut]

Fit a quantile regression neural network with optional
left censoring using a variant of the finite smoothing
algorithm.

Fit quantile regression neural network models with optional
left censoring, partial monotonicity constraints, generalized additive
model constraints, and the ability to fit multiple non-crossing quantile
functions following Cannon (2011) <doi:10.1016/j.cageo.2010.07.005>
and Cannon (2017) <doi:10.17605/OSF.IO/WG7SN>.