Implements assessment of benefit-risk balance using Bayesian Discrete Choice Experiment. For more details see the article by Mukhopadhyay et al. (2019) <DOI:10.1080/19466315.2018.1527248>.

Calculate expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for a several component vaccine. Uses Hill models for each component which are combined under Bliss independence. See Saul and Fay, 2007 <DOI:10.1371/journal.pone.0000850>.

Contains functions for fitting hierarchical versions of
        EVSD, UVSD, DPSD, DPSD with d' restricted to be positive, and
        our gamma signal detection model to recognition memory
        confidence-ratings data.

Functions to compute small area estimates based on a basic area or
    unit-level model. The model is fit using restricted maximum likelihood, or
    in a hierarchical Bayesian way. In the latter case numerical integration is
    used to average over the posterior density for the between-area variance.
    The output includes the model fit, small area estimates and corresponding
    mean squared errors, as well as some model selection measures. Additional functions
    provide means to compute aggregate estimates and mean squared errors, to minimally
    adjust the small area estimates to benchmarks at a higher aggregation
    level, and to graphically compare different sets of small area estimates.

Fits area-level Hierarchical Bayesian Small Area Estimation
    models. The methodological foundation follows the standard area-level
    Small Area Estimation literature, primarily Rao and Molina (2015,
    ISBN: 9781118735787) <doi:10.1002/9781118735855>, while computational
    implementation is adapted to the parameterisation and
    prior-specification conventions of the 'brms' package
    <doi:10.18637/jss.v080.i01>, which targets the
    Stan back-end. Supports a principled Bayesian workflow
    <doi:10.48550/arXiv.2011.01808>, with prior
    predictive checks, convergence diagnostics, model comparison,
    spatial random effects, custom distributions, missing-data
    handling, and a bilingual 'shiny' application for non-programmer
    analysts.