An implementation of the Bayesian version of the Mallows rank model
    (Vitelli et al., Journal of Machine Learning Research, 2018 <https://jmlr.org/papers/v18/15-481.html>;
    Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>;
    Sorensen et al., R Journal, 2020 <doi:10.32614/RJ-2020-026>;
    Stein, PhD Thesis, 2023 <https://eprints.lancs.ac.uk/id/eprint/195759>). Both Metropolis-Hastings
    and sequential Monte Carlo algorithms for estimating the models are available. 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>).

Provides nested sequential Monte Carlo algorithms for performing
    sequential inference in the Bayesian Mallows model, which is a widely used
    probability model for rank and preference data. The package implements the
    SMC2 (Sequential Monte Carlo Squared) algorithm for handling sequentially
    arriving rankings and pairwise preferences, including support for complete
    rankings, partial rankings, and pairwise comparisons. The methods are based
    on Sorensen (2025) <doi:10.1214/25-BA1564>.

Computes Bayesian posterior distributions of predictions, marginal effects, and differences of marginal effects for various generalized linear models. Importantly, the posteriors are on the mean (response) scale, allowing for more natural interpretation than summaries on the link scale. Also, predictions and marginal effects of the count probabilities for Poisson and negative binomial models can be computed.