Suggests:      gam, splines, MASS, class, mclust02, leaps, mda, lasso2,
Packaged: Wed Mar  7 13:32:25 2007; halvorsen

Packaged: Fri Sep 28 19:52:32 2007; halvorsen

Packaged: Thu Feb 12 17:38:21 2009; hornik

Title: Data sets, functions and examples from the book: "The Elements
of Statistical Learning, Data Mining, Inference, and
Prediction" by Trevor Hastie, Robert Tibshirani and Jerome
Author: Material from the book's webpage, R port and packaging by
Description: Useful when reading the book above mentioned, in the
Suggests: gam, splines, MASS, class, mclust02, leaps, mda, lasso2,

Suggests:      gam, splines, MASS, class, mclust, leaps, mda, lasso2, lars, boot
Packaged: Thu Oct  6 16:21:09 2005; halvorsen

Suggests: gam, splines, MASS, class, leaps, mda, lasso2, lars, boot,

Title: Data Sets, Functions and Examples from the Book: "The Elements
Maintainer: Kjetil B Halvorsen <kjetil1001@gmail.com>

Author: Material from the book's webpage
R port and packaging by Kjetil B Halvorsen
X-CRAN-Original-Maintainer: Kjetil B Halvorsen <kjetil1001@gmail.com>
X-CRAN-Comment: Orphaned and corrected on 2019-03-02 as check issues

Title: Inference of Elliptical Distributions and Copulas
Description: Provides functions for the simulation and
the nonparametric estimation of elliptical distributions,
meta-elliptical copulas and trans-elliptical distributions,
following the article Derumigny and Fermanian (2021) <arXiv:2106.12367>.
person("Jean-David", "Fermanian",, email = "Jean-David.Fermanian@ensae.fr", role = "aut", comment = c(ORCID = "0000-0001-5960-5555"))

following the article Derumigny and Fermanian (2022) <doi:10.1016/j.jmva.2022.104962>.
person("Jean-David", "Fermanian",, email = "Jean-David.Fermanian@ensae.fr", role = "aut", comment = c(ORCID = "0000-0001-5960-5555")),
person("Rutger", "van der Spek",, role = "ctb")
Imports: Runuran, pcaPP, Matrix, Rmpfr, pbapply

Imports: Runuran, wdm, Matrix, Rmpfr, pbapply

Title: Preference Rating for Visual Stimuli Based on Elo Ratings
Description: Allows calculating global scores for characteristics of visual stimuli. Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses  preference for one stimulus over the other. The algorithm for calculating global scores is based on Elo rating, which updates individual scores after each single pairwise contest. Elo rating is widely used to rank chess players according to their performance. Its core feature is that dyadic contests with expected outcomes lead to smaller changes of participants' scores than outcomes that were unexpected. As such, Elo rating is an efficient tool to rate individual stimuli when a large number of such stimuli are paired against each other in the context of experiments where the goal is to rank stimuli according to some characteristic of interest.