rmallows

Vector specifying the latent consensus ranking.

rho0

Scalar specifying the scale parameter.

alpha0

Integer specifying the number of random samples to generate.

n_samples

Integer specifying the number of iterations to discard as burn-in.

burnin

Integer specifying the number of MCMC iterations to perform
between each time a random rank vector is sampled.

thinning

Integer specifying the step size of the leap-and-shift proposal distribution.

leap_size

Character string specifying the distance measure to use. Available
options are <code>"footrule"</code> (default), <code>"spearman"</code>, <code>"cayley"</code>, <code>"hamming"</code>,
<code>"kendall"</code>, and <code>"ulam"</code>. For sampling from the Mallows model with Cayley, Hamming, Kendall,
and Ulam distances
the <code>PerMallows</code> package irurozki2016BayesMallows can also be used.

metric

Sample from the Mallows distribution with arbitrary distance metric using
a Metropolis-Hastings algorithm.

internal

An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 <http://jmlr.org/papers/v18/15-481.html>; Crispino et al., to appear in Annals of Applied Statistics). Both 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>).

rmallows: Sample from the Mallows distribution.

Description

Usage

Arguments

References