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 bamlss, 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.

## References

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 (2021). bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond). Journal of Statistical Software, 100(4), 1--53. doi: 10.18637/jss.v100.i04

bamlss, bamlss.frame