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

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BayesMallows documentation built on Sept. 11, 2024, 5:31 p.m.