Packaged: 2019-01-09 20:53:43 UTC; sebastian

a set of covariates, see Dimitriadis and Bayer (2019) <doi:10.1214/19-EJS1560>.
Packaged: 2019-11-15 20:28:51 UTC; sebastian

Packaged: 2022-04-09 22:00:13 UTC; sebastian

Title: Download Data from the European Social Survey on the Fly
Authors@R: person("Jorge", "Cimentada", email = "cimentadaj@gmail.com", role = c("aut", "cre"))
Description: 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.
Imports: XML, xml2, httr, stringr, haven
Packaged: 2017-11-07 14:26:33 UTC; cimentadaj

Imports: xml2, httr, stringr, haven, rvest, tibble
Note: 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
Packaged: 2018-03-02 16:41:01 UTC; cimentadaj

Title: Efficient Stepwise Selection in Decomposable Models
Description: 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.

Description: 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>.

Description: Provide an optimal histogram, in the sense of probability density estimation and features detection, by means of multiscale variational inference. In other words, the resulting histogram servers as an optimal density estimator, and meanwhile recovers the features, such as increases or modes, with both false positive and false negative controls. Moreover, it provides a parsimonious representation in terms of the number of blocks, which simplifies data interpretation. The only assumption for the method is that data points are independent and identically distributed, so it applies to fairly general situations, including continuous distributions, discrete distributions, and mixtures of both. For details see Li, Munk, Sieling and Walther (2016) <arXiv:1612.07216>.
Imports: Rcpp (>= 0.12.5), graphics, stats, grDevices, utils