Fits Bayesian models (amongst others) to dissolution data sets that can be used for dissolution testing. The package was originally constructed to include only the Bayesian models outlined in Pourmohamad et al. (2022) <doi:10.1111/rssc.12535>. However, additional Bayesian and non-Bayesian models (based on bootstrapping and generalized pivotal quanties) have also been added. More models may be added over time.

Functions for data augmentation using the Bayesian discount prior 
    method for single arm and two-arm clinical trials, as described in Haddad 
    et al. (2017) <doi:10.1080/10543406.2017.1300907>. The discount power prior 
    methodology was developed in collaboration with the The Medical Device 
    Innovation Consortium (MDIC) Computer Modeling & Simulation Working Group.

Exploratory Bayesian factor analysis of continuous, mixed-type, and bounded continuous variables using the mode-jumping algorithm of Man and Culpepper (2020) <doi:10.1080/01621459.2020.1773833>.

A  Bayesian smoothing method for post-processing of remote 
    sensing image classification which refines the
    labelling in a classified image in order to enhance its classification accuracy.
    Combines pixel-based classification methods with a spatial post-processing
    method to remove outliers and misclassified pixels.

Suite of tools that facilitate
    exposure-response analysis using Bayesian methods. The package
    provides a streamlined workflow for fitting types of models that are
    commonly used in exposure-response analysis - linear and Emax for continuous
    endpoints, logistic linear and logistic Emax for binary endpoints, as well
    as performing simulation and visualization. Learn more about the workflow
    at <https://genentech.github.io/BayesERbook/>.

Determines effective sample size of a parametric prior distribution
    in Bayesian models. For a web-based Shiny application related to this package, see <https://implement.shinyapps.io/bayesess/>.

A Bayesian framework to estimate the Student's t-distribution's degrees of freedom is developed. Markov Chain Monte Carlo sampling routines are developed as in <doi:10.3390/axioms11090462> to sample from the posterior distribution of the degrees of freedom. A random walk Metropolis algorithm is used for sampling when Jeffrey's and Gamma priors are endowed upon the degrees of freedom. In addition, the Metropolis-adjusted Langevin algorithm for sampling is used under the Jeffrey's prior specification. The Log-normal prior over the degrees of freedom is posed as a viable choice with comparable performance in simulations and real-data application, against other prior choices, where an Elliptical Slice Sampler is used to sample from the concerned posterior.