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. Bayes 
    factors for these three tests can be computed based on raw data (x, y) or 
    summary statistics (n_x, n_y, mean_x, mean_y, sd_x, sd_y [or ci_margin 
    and ci_level]).

Performs general Bayesian estimation method of linear–bilinear models for genotype × environment interaction. The method is explained in Perez-Elizalde, S., Jarquin, D., and Crossa, J. (2011) (<doi:10.1007/s13253-011-0063-9>).

Fit two-regime threshold autoregressive (TAR) models by Markov chain Monte Carlo methods.

Enable users to evaluate long-term trends using a Generalized 
    Additive Modeling (GAM) approach. The model development includes selecting a 
    GAM structure to describe nonlinear seasonally-varying changes over time, 
    incorporation of hydrologic variability via either a river flow or salinity, 
    the use of an intervention to deal with method or laboratory changes 
    suspected to impact data values, and representation of left- and 
    interval-censored data. The approach has been applied to water quality data 
    in the Chesapeake Bay, a major estuary on the east coast of the United 
    States to provide insights to a range of management- and research-focused 
    questions.  Methodology described in Murphy (2019) 
    <doi:10.1016/j.envsoft.2019.03.027>.