Functions for exploring and visualising estimation results
  obtained with BayesX, a free software for estimating structured additive
  regression models (<https://www.uni-goettingen.de/de/bayesx/550513.html>). In addition,
  functions that allow to read, write and manipulate map objects that are required in spatial
  analyses performed with BayesX.

'BayesX' performs Bayesian inference in structured additive regression (STAR) models.
	The R package BayesXsrc provides the 'BayesX' command line tool for easy installation.
	A convenient R interface is provided in package R2BayesX.

Fits a Bayesian zero-inflated Bernoulli regression model handling (potentially) different covariates for the zero-inflated
    and non zero-inflated parts. See Moriña D, Puig P, Navarro A. (2021) <doi:10.1186/s12874-021-01427-2>.

A Bayesian, global planktic foraminifera core top calibration to 
    modern sea-surface temperatures. Includes four calibration models, 
    considering species-specific calibration parameters and seasonality.

This R package offers block Gibbs samplers for the Bayesian (adaptive) graphical lasso, ridge, and naive elastic net priors. These samplers facilitate the simulation of the posterior distribution of precision matrices for Gaussian distributed data and were originally proposed by: Wang (2012) <doi:10.1214/12-BA729>; Smith et al. (2022) <doi:10.48550/arXiv.2210.16290> and Smith et al. (2023) <doi:10.48550/arXiv.2306.14199>, respectively.

Bayesian analysis of luminescence data and C-14 age
    estimates. Bayesian models are based on the following publications:
    Combes, B. & Philippe, A. (2017) <doi:10.1016/j.quageo.2017.02.003>
    and Combes et al. (2015) <doi:10.1016/j.quageo.2015.04.001>. This
    includes, amongst others, data import, export, application of age
    models and palaeodose model.

Bayesian approach to multidimensional scaling. The package consists of implementations of the methods of Oh and Raftery (2001)  <doi:10.1198/016214501753208690>.

BAYesian inference for MEDical designs in R. Functions for the 
    computation of Bayes factors for common biomedical research designs. 
    Implemented are functions to test the equivalence (equiv_bf), 
    non-inferiority (infer_bf), and superiority (super_bf) of an experimental 
    group compared to a control group on a continuous outcome measure, as well 
    as functions for simulating survival data and calculating a Bayes factor 
    for Cox proportional hazards models. Bayes factors for these tests can be 
    computed based on raw data or summary statistics.