marginal

n dimensional distance decomposition vector where h_j = 0 means that $j$ is a fixed point,
h_j = 1 means that $j$ is an unfixed point and otherwise $j$ is not known

n dimensional distance decomposition vector with the dispersion parameters

theta

Compute the marginal probability, GMM under the Hamming distance,
of a distance decomposition vector for which some positions are known and some are not

This package includes functions to work with the Mallows, Generalized Mallows and Weighted Mallows models. The considered distances are Kendall's $\tau$, Cayley, Hamming and Ulam and it includes functions for making inference, sampling and learning such distributions, some of which are novel in the literature. As a by-product, PerMallows also includes operations for permutations, paying special attention in those related with the Kendall's-$\tau$, Cayley, Ulam and Hamming distances and the random generation of permutations.

marginal: Compute the marginal probability, GMM under the Hamming distance

Description

Usage

Arguments

Value

Examples