sounds

<p>Data from an experiment in which 46 individuals compared 12 different sounds
barrett2018BayesMallows. Each assessor was asked multiple
times to compare a pair of two sounds, indicating which of the sounds sounded
the most like it was human generated. The pairwise preference for each
assessor are in general non-transitive. These data inspired the Mallows model
for non-transitive pairwise preferences developed by
crispino2019BayesMallows.</p>

datasets

An implementation of the Bayesian version of the Mallows rank model
(Vitelli et al., Journal of Machine Learning Research, 2018 <https://jmlr.org/papers/v18/15-481.html>;
Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>;
Sorensen et al., R Journal, 2020 <doi:10.32614/RJ-2020-026>;
Stein, PhD Thesis, 2023 <https://eprints.lancs.ac.uk/id/eprint/195759>). Both Metropolis-Hastings
and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule,
Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be
analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well
as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the
posterior distributions of parameters are provided. The package also provides functions for estimating
the partition function (normalizing constant) of the Mallows rank model, both with the importance
sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm
(Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).

Oystein Sorensen

BayesMallows

Bayesian Preference Learning with the Mallows Rank Model

Waldir Leoncio

Valeria Vitelli

Marta Crispino

Qinghua Liu

Cristina Mollica

Luca Tardella

Anja Stein

sounds function

<p>An object of class <code>data.frame</code> with 1380 rows and 3 columns.</p>

Format

Sounds data — sounds

Sounds data

sounds: Sounds data

Description

Usage

Arguments

Format

References

See Also