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Set parameters related to the Metropolis-Hastings algorithm.


  nmc = 2000,
  burnin = NULL,
  alpha_prop_sd = 0.1,
  rho_proposal = c("ls", "swap"),
  leap_size = 1,
  aug_method = c("uniform", "pseudo"),
  pseudo_aug_metric = c("footrule", "spearman"),
  swap_leap = 1,
  alpha_jump = 1,
  aug_thinning = 1,
  clus_thinning = 1,
  rho_thinning = 1,
  include_wcd = FALSE,
  save_aug = FALSE,
  save_ind_clus = FALSE



Integer specifying the number of iteration of the Metropolis-Hastings algorithm to run. Defaults to 2000. See assess_convergence() for tools to check convergence of the Markov chain.


Integer defining the number of samples to discard. Defaults to NULL, which means that burn-in is not set.


Numeric value specifying the \(\sigma\) parameter of the lognormal proposal distribution used for \(\alpha\) in the Metropolis-Hastings algorithm. The logarithm of the proposed samples will have standard deviation given by alpha_prop_sd. Defaults to 0.1.


Character string specifying the proposal distribution of modal ranking \(\rho\). Defaults to "ls", which means that the leap-and-shift algorithm of Vitelli et al. (2018) is used. The other option is "swap", which means that the swap proposal of Crispino et al. (2019) is used instead.


Integer specifying the step size of the distribution defined in rho_proposal for proposing new latent ranks \(rho\). Defaults to 1.


Augmentation proposal for use with missing data. One of "pseudo" and "uniform". Defaults to "uniform", which means that new augmented rankings are proposed by sampling uniformly from the set of available ranks, see Section 4 in Vitelli et al. (2018) . Setting the argument to "pseudo" instead, means that the pseudo-likelihood proposal defined in Chapter 5 of Stein (2023) is used instead.


String defining the metric to be used in the pseudo-likelihood proposal. Only used if aug_method = "pseudo". Can be either "footrule" or "spearman", and defaults to "footrule".


Integer specifying the leap size for the swap proposal used for proposing latent ranks in the case of non-transitive pairwise preference data. Note that leap size for the swap proposal when used for proposal the modal ranking \(\rho\) is given by the leap_size argument above.


Integer specifying how many times to sample \(\rho\) between each sampling of \(\alpha\). In other words, how many times to jump over \(\alpha\) while sampling \(\rho\), and possibly other parameters like augmented ranks \(\tilde{R}\) or cluster assignments \(z\). Setting alpha_jump to a high number can speed up computation time, by reducing the number of times the partition function for the Mallows model needs to be computed. Defaults to 1.


Integer specifying the thinning for saving augmented data. Only used when save_aug = TRUE. Defaults to 1.


Integer specifying the thinning to be applied to cluster assignments and cluster probabilities. Defaults to 1.


Integer specifying the thinning of rho to be performed in the Metropolis- Hastings algorithm. Defaults to 1. compute_mallows save every rho_thinningth value of \(\rho\).


Logical indicating whether to store the within-cluster distances computed during the Metropolis-Hastings algorithm. Defaults to FALSE. Setting include_wcd = TRUE is useful when deciding the number of mixture components to include, and is required by plot_elbow().


Logical specifying whether or not to save the augmented rankings every aug_thinningth iteration, for the case of missing data or pairwise preferences. Defaults to FALSE. Saving augmented data is useful for predicting the rankings each assessor would give to the items not yet ranked, and is required by plot_top_k().


Whether or not to save the individual cluster probabilities in each step. This results in csv files cluster_probs1.csv, cluster_probs2.csv, ..., being saved in the calling directory. This option may slow down the code considerably, but is necessary for detecting label switching using Stephen's algorithm.


An object of class "BayesMallowsComputeOptions", to be provided in the compute_options argument to compute_mallows(), compute_mallows_mixtures(), or update_mallows().


Crispino M, Arjas E, Vitelli V, Barrett N, Frigessi A (2019). “A Bayesian Mallows approach to nontransitive pair comparison data: How human are sounds?” The Annals of Applied Statistics, 13(1), 492--519. doi:10.1214/18-aoas1203 .

Stein A (2023). Sequential Inference with the Mallows Model. Ph.D. thesis, Lancaster University.

Vitelli V, Sørensen, Crispino M, Arjas E, Frigessi A (2018). “Probabilistic Preference Learning with the Mallows Rank Model.” Journal of Machine Learning Research, 18(1), 1--49.