The bamlss package is a general tool for complex Bayesian regression modeling
with structured additive predictors based on Markov chain Monte Carlo simulation.
The design of this package substantially focuses on maximum flexibility and easy
integration of new code and/or standalone systems. The package makes heavy use of
mgcv infrastructures to build up all necessary model matrices and
information from which it is relatively easy for the user to construct estimation
algorithms or interfaces to existing software packages. The package can also be
seen as an harmonized framework for regression modeling since it does not restrict
to any type of problem. The main function in this package is
which is a wrapper function that calls optimizer and/or sampling functions for fitting
Bayesian additive models for location scale and shape (and beyond). These model fitting
functions can be exchanged by the user. Moreover, the package contains numerous
functions for creating post-estimation results like summary statistics and
effect plots etc.
Umlauf N, Klein N, Zeileis A (2019). BAMLSS: Bayesian Additive Models for Location, Scale and Shape (and Beyond). Journal of Computational and Graphical Statistics, 27(3), 612--627. doi: 10.1080/10618600.2017.1407325
Umlauf N, Klein N, Simon T, Zeileis A (2019). “bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond).” arXiv:1909.11784, arXiv.org E-Print Archive. https://arxiv.org/abs/1909.11784