set_progress_report

Specify whether progress should be reported, and how often.

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

set_progress_report function

<dl><dt>verbose</dt>
<dd>Boolean specifying whether to report progress or not. Defaults
to <code>FALSE</code>.</dd>
<dt>report_interval</dt>
<dd>Strictly positive number specifying how many
iterations of MCMC should be run between each progress report. Defaults to
<code>1000</code>.</dd></dl>

Arguments

Set progress report options for MCMC algorithm — set_progress_report

<dl>

<dt>verbose</dt>
<dd>Boolean specifying whether to report progress or not. Defaults
to <code>FALSE</code>.</dd>


<dt>report_interval</dt>
<dd>Strictly positive number specifying how many
iterations of MCMC should be run between each progress report. Defaults to
<code>1000</code>.</dd>

</dl>

set_progress_report: Set progress report options for MCMC algorithm

Description

Usage

Value

Arguments

References

See Also