'Kevin Dowd's' book Measuring Market Risk is a widely read book
in the area of risk measurement by students and
practitioners alike. As he claims, 'MATLAB' indeed might have been the most
suitable language when he originally wrote the functions, but,
with growing popularity of R it is not entirely
valid. As 'Dowd's' code was not intended to be error free and were mainly
for reference, some functions in this package have inherited those
errors. An attempt will be made in future releases to identify and correct
them. 'Dowd's' original code can be downloaded from www.kevindowd.org/measuring-market-risk/.
It should be noted that 'Dowd' offers both
'MMR2' and 'MMR1' toolboxes. Only 'MMR2' was ported to R. 'MMR2' is more
recent version of 'MMR1' toolbox and they both have mostly similar
function. The toolbox mainly contains different parametric and non
parametric methods for measurement of market risk as well as
backtesting risk measurement methods.

Harry Joo [aut, cre], Herman Aguinis [aut, dtc], Kyle J. Bradley [aut], Takuya Noguchi [ctb]
Compares distributions with one another in terms of their fit to each sample in a dataset that contains multiple samples, as described in Joo, Aguinis, and Bradley (in press). Users can examine the fit of seven distributions per sample: pure power law, lognormal, exponential, power law with an exponential cutoff, normal, Poisson, and Weibull. Automation features allow the user to compare all distributions for all samples with a single command line, which creates a separate row containing results for each sample until the entire dataset has been analyzed.

Data Representation: Bayesian Approach That's Sparse
c(person("Gabrielle", "Weinrott", email = "gabrielle.weinrott@supagro.inra.fr", role = c("aut", "cre")), person("Brigitte", "Charnomordic", email = "bch@supagro.inra.fr", role = "aut"), person("Benedicte", "Fontez", email = "benedicte.fontez@supagro.fr", role = "aut"), person("Nadine", "Hilgert", email = "nadine.hilgert@supagro.inra.fr", role = "aut"), person("Susan", "Holmes", email = "susan@stat.stanford.edu", role = "aut"))
Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016.
fda, ggplot2, knitr, parallel, rmarkdown, testthat
Gabrielle Weinrott [aut, cre],
Brigitte Charnomordic [aut],
Benedicte Fontez [aut],
Nadine Hilgert [aut],
Susan Holmes [aut]