Bayes Watch fits an array of Gaussian Graphical Mixture Models to groupings of homogeneous data in time, called regimes, which are modeled as the observed states of a Markov process with unknown transition probabilities.  In doing so, Bayes Watch defines a posterior distribution on a vector of regime assignments, which gives meaningful expressions on the probability of every possible change-point.  Bayes Watch also allows for an effective and efficient fault detection system that assesses what features in the data where the most responsible for a given change-point.  For further details, see: Alexander C. Murph et al. (2023) <arXiv:2310.02940>.

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.