get_sample_probabilities

An integer value rank of an item in the current consensus ranking

rho_item_rank

Numeric value of the scale parameter

alpha

A sequence of integer values of the set of possible ranks that we can assign the item

remaining_set_ranks

A character string specifying the distance metric to use in the
Bayesian Mallows Model. Available options are <code>"footrule"</code>,
<code>"spearman"</code>, <code>"cayley"</code>, <code>"hamming"</code>, <code>"kendall"</code>, and
<code>"ulam"</code>.

metric

Integer is the number of items in the consensus ranking

n_items

Calculate probability of assigning a set of specific ranks to an specific item
given its rank in the consensus ranking

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>). 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>).

get_sample_probabilities: Get Sample Probabilities

Description

Usage

Arguments

Value