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.

A collection of functions allowing to derive the posterior distribution of the model parameters in random-effects meta-analysis or meta-regression, and providing functionality to evaluate joint and marginal posterior probability distributions, predictive distributions, shrinkage effects, posterior predictive p-values, etc.; For more details, see also Roever C (2020) <doi:10.18637/jss.v093.i06>, or Roever C and Friede T (2022) <doi:10.1016/j.cmpb.2022.107303>.

Contains a split population survival estimator that models the 
    misclassification probability of failure versus right-censored events.  
    The split population survival estimator is described in 
    Bagozzi et al. (2019) <doi:10.1017/pan.2019.6>.

Producing probabilistic projections of net migration rate for all countries of the world
    or for subnational units using a Bayesian hierarchical model by Azose an Raftery (2015) <doi:10.1007/s13524-015-0415-0>.

Fits finite mixture models of univariate Gaussian distributions using JAGS within a Bayesian framework.

Bayesian Mixture Survival Models using Additive Mixture-of-Weibull Hazards, with Lasso Shrinkage and
        Stratification. As a Bayesian dynamic survival model, it relaxes the proportional-hazard assumption. Lasso shrinkage controls
        overfitting, given the increase in the number of free parameters in the model due to presence of two Weibull components
        in the hazard function.