Simulating synthetic clumped isotope dataset, fitting
    linear regression models under Bayesian and non-Bayesian frameworks,
    and generating temperature reconstructions for the same two approaches.
    Please note that models implemented in this package are described
    in Roman-Palacios et al. (2021) <doi:10.1002/essoar.10507995.1>.

Implements the Bayesian FDR control described by 
    Newton et al. (2004), <doi:10.1093/biostatistics/5.2.155>.
    Allows optimisation and visualisation of expected error rates based on
    tail posterior probability tests.
    Based on code written by Catalina Vallejos for BASiCS, see
    Beyond comparisons of means: understanding changes in gene expression at the
    single-cell level Vallejos et al. (2016) <doi:10.1186/s13059-016-0930-3>.

As heavy-tailed error distribution and outliers in the response variable widely exist, models which are robust to data contamination are highly demanded. Here, we develop a novel robust Bayesian variable selection method with elastic net penalty. In particular, the spike-and-slab priors have been incorporated to impose sparsity. An efficient Gibbs sampler has been developed to facilitate computation.The core modules of the package have been developed in 'C++' and R.

Contains several Bayesian models for data analysis of psychological tests. A user friendly interface for these models should enable students and researchers to perform professional level Bayesian data analysis without advanced knowledge in programming and Bayesian statistics. This package is based on the Stan platform (Carpenter et el. 2017 <doi:10.18637/jss.v076.i01>).

Performs Bayesian estimation of the additive main effects and multiplicative interaction (AMMI) model. The method is explained in Crossa, J., Perez-Elizalde, S., Jarquin, D., Cotes, J.M., Viele, K., Liu, G. and Cornelius, P.L. (2011) (<doi:10.2135/cropsci2010.06.0343>).

Provides a Bayesian version of the analysis of variance based on a three-component Gaussian mixture for which a Gibbs sampler produces posterior draws. For details about the Bayesian ANOVA based on Gaussian mixtures, see Kelter (2019) <arXiv:1906.07524>.