rmallows: Sample from the Mallows distribution.

In BayesMallows: Bayesian Preference Learning with the Mallows Rank Model

Sample from the Mallows distribution.

Description

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

Usage

rmallows(
  rho0,
  alpha0,
  n_samples,
  burnin,
  thinning,
  leap_size = 1L,
  metric = "footrule"
)

Arguments

rho0	Vector specifying the latent consensus ranking.
alpha0	Scalar specifying the scale parameter.
n_samples	Integer specifying the number of random samples to generate.
burnin	Integer specifying the number of iterations to discard as burn-in.
thinning	Integer specifying the number of MCMC iterations to perform between each time a random rank vector is sampled.
leap_size	Integer specifying the step size of the leap-and-shift proposal distribution.
metric	Character string specifying the distance measure to use. Available options are "footrule" (default), "spearman", "cayley", "hamming", "kendall", and "ulam".

References

\insertAllCited

BayesMallows documentation built on Nov. 25, 2023, 5:09 p.m.