  Uncertainty quantification using Bayesian methods is a growing area of
research. Bayesian model mixing (BMM) is a recent development which combines
the predictions from multiple models such that each model's best qualities are
preserved in the final result. Practical tools and analysis suites that
facilitate such methods are therefore needed. Taweret introduces BMM to
existing Bayesian uncertainty quantification efforts. Currently Taweret
contains three individual Bayesian model mixing techniques, each pertaining to
a different type of problem structure; we encourage the future inclusion of
user-developed mixing methods. Taweret's first use case is in nuclear physics,
but the package has been structured such that it should be adaptable to any
research engaged in model comparison or model mixing.
