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.

A suite of functions for computing
    various Bayes factors for simple designs, including contingency tables,
    one- and two-sample designs, one-way designs, general ANOVA designs, and
    linear regression.

Allows Bayesian borrowing from a historical dataset for time-to-
    event data. A flexible baseline hazard function is achieved via a piecewise
    exponential likelihood with time varying split points and smoothing prior on the
    historic baseline hazards. The method is described in Scott and Lewin (2024) 
    <doi:10.48550/arXiv.2401.06082>, and the software paper is in Axillus et al. 
    (2024) <doi:10.48550/arXiv.2408.04327>.