get_partition_function

n_items

alpha

Number of occurrences for each unique distance.
Applicable for footrule and Spearman distance. Defaults to <code>R_NilValue</code>.

cardinalities

Precomputed importance sampling fit.

logz_estimate

A string. Available options are <code>"footrule"</code>,
<code>"kendall"</code>, <code>"spearman"</code>, <code>"cayley"</code>, and <code>"hamming"</code>.
Defaults to <code>"footrule"</code>.

metric

Compute the logarithm of the partition function for a Mallows rank model.

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>).

get_partition_function: Compute the logarithm of the partition function for a Mallows rank model.

Description

Usage

Arguments

Value