datamods (>= 1.2.0), ggplot2 (>= 3.0.0), grDevices, htmltools
(>= 0.5.0), jsonlite, phosphoricons, rlang (>= 0.3.1),
rstudioapi, scales, shiny (>= 1.1.0), shinyWidgets (>= 0.6.0)

Joint Quantile and Expected Shortfall Regression
c(person('Sebastian', 'Bayer', email='sebastian.bayer@uni-konstanz.de', role=c('aut', 'cre')),
person('Timo', 'Dimitriadis', role='aut'))
Simultaneous modeling of the quantile and the expected shortfall of a response variable given
a set of covariates, see Dimitriadis and Bayer (2017) <arXiv:1704.02213>.

c(person('Sebastian', 'Bayer', email='sebastian.bayer@uni-konstanz.de', role=c('aut', 'cre')),
person('Timo', 'Dimitriadis', email='timo.dimitriadis@uni-konstanz.de', role='aut'))

Simultaneous modeling of the quantile and the expected shortfall of a response variable given
a set of covariates, see Dimitriadis and Bayer (2019) <doi:10.1214/19-EJS1560>.

Download Data from the European Social Survey on the Fly
person("Jorge", "Cimentada", email = "cimentadaj@gmail.com", role = c("aut", "cre"))
Download data from the European Social Survey directly from their website <http://www.europeansocialsurvey.org/>. There are two family of functions that allow you to download and interactively check all countries and rounds available.

xml2, httr, stringr, haven, rvest, tibble
The ess package is named to match the European Social Survey
(ESS) hosted at http://www.europeansocialsurvey.org/. It is
unrelated to the Emacs Speaks Statistics (ESS) project, an
emacs-based Integrated Development Environment hosted at
https://ess.r-project.org

Efficient Stepwise Selection in Decomposable Models
person(given = "Mads",
family = "Lindskou",
role = c("aut", "cre"),
email = "mads@math.aau.dk")
An implementation of the ESS algorithm following Amol Deshpande, Minos Garofalakis,
Michael I Jordan (2013) <arXiv:1301.2267>. The ESS algorithm
is used for model selection in decomposable graphical models.

Provide an optimal histogram, in the sense of probability density estimation and features detection, by means of multiscale variational inference. For details see Li, Munk, Sieling and Walther (2016) <arXiv:1612.07216>.