Methods for assessing animal movement from telemetry and biologging
    data using non-parametric Bayesian methods. This includes features for pre-
    processing and analysis of data, as well as the visualization of results
    from the models. This framework does not rely on standard parametric density
    functions, which provides flexibility during model fitting. Further details 
    regarding part of this framework can be found in Cullen et al. (2021) <doi:10.1101/2020.11.05.369702>.

Bayesian analysis of multivariate receptor modeling. The package consists of implementations of the methods of Park and Oh (2015) <doi:10.1016/j.chemolab.2015.08.021>.The package uses 'JAGS'(Just Another Gibbs Sampler) to generate Markov chain Monte Carlo samples of parameters.

A two-step Bayesian approach for mode inference following 
      Cross, Hoogerheide, Labonne and van Dijk (2024) <doi:10.1016/j.econlet.2024.111579>).
      First, a mixture distribution is fitted on the data using a sparse finite
      mixture (SFM) Markov chain Monte Carlo (MCMC) algorithm. The number of
      mixture components does not have to be known; the size of the mixture is
      estimated endogenously through the SFM approach. Second, the modes of the
      estimated mixture at each MCMC draw are retrieved using algorithms
      specifically tailored for mode detection. These estimates are then used to
      construct posterior probabilities for the number of modes, their locations
      and uncertainties, providing a powerful tool for mode inference.

Objective Bayesian inference procedures for the parameters of the
    multivariate random effects model with application to multivariate
    meta-analysis. The posterior for the model parameters, namely the overall
    mean vector and the between-study covariance matrix, are assessed by
    constructing Markov chains based on the Metropolis-Hastings algorithms as
    developed in Bodnar and Bodnar (2021) (<arXiv:2104.02105>). The
    Metropolis-Hastings algorithm is designed under the assumption of the
    normal distribution and the t-distribution when the Berger and Bernardo
    reference prior and the Jeffreys prior are assigned to the model parameters.
    Convergence properties of the generated Markov chains are investigated by
    the rank plots and the split hat-R estimate based on the rank normalization,
    which are proposed in Vehtari et al. (2021) (<DOI:10.1214/20-BA1221>).