Provides a Bayesian framework for Dynamic Energy Budget (DEB)
    modelling via 'Stan'. Implements the standard DEB model of Kooijman
    (2010, <doi:10.1017/CBO9780511805400>) as a state-space model with
    Hamiltonian Monte Carlo inference (Carpenter et al., 2017,
    <doi:10.18637/jss.v076.i01>). Includes individual-level growth models,
    growth-reproduction models, hierarchical multi-individual models with
    partial pooling, and toxicokinetic-toxicodynamic (TKTD) models for
    ecotoxicology following the DEBtox framework (Jager et al., 2006,
    <doi:10.1007/s10646-006-0060-x>). Supports prior specification from
    biological knowledge, convergence diagnostics (Vehtari et al., 2021,
    <doi:10.1214/20-BA1221>), posterior predictive checks, derived quantity
    estimation, and visualisation via 'ggplot2'.

Disaggregates an observed aggregate price index into sectoral
    components with a Bayesian state-space model in which the aggregate enters as
    a genuine observation density rather than as a renormalization identity. A
    random-walk-with-drift transition in log space (with partial pooling on the
    drift and the innovation scale) and an estimable cross-sectional
    concentration produce posterior draws of the sectoral indices with credible
    intervals, suitable as multiple-imputation input for downstream dynamic
    models. The Hamiltonian Monte Carlo engine follows Stan (Carpenter et al.,
    2017) <doi:10.18637/jss.v076.i01>; model comparison uses Pareto Smoothed
    Importance Sampling Leave-One-Out cross-validation (Vehtari, Gelman and
    Gabry, 2017) <doi:10.1007/s11222-016-9696-4>. A closed-form linear-Gaussian
    Kalman/RTS smoother provides an exact, MCMC-free Bayesian alternative for the
    same aggregate evidence.

The Epidemic Type Aftershock Sequence (ETAS) model is widely
    used for modelling and forecasting earthquake occurrences. This package
    implements Bayesian estimation routines for both the temporal and spatial ETAS
    model, allowing samples to be drawn from the full posterior distribution
    of the model parameters given an earthquake catalogue. The methods are
    described in Ross (2021) "Bayesian Estimation of the ETAS Model for
    Earthquake Occurrences" <doi:10.1785/0120200198>.