  agtboost is an R package implementing fast gradient tree boosting
computations in a manner similar to other established frameworks such as
xgboost and LightGBM, but with significant decreases in computation time and
required mathematical and technical knowledge. The package automatically takes
care of split/no-split decisions and selects the number of trees in the
gradient tree boosting ensemble, i.e., agtboost adapts the complexity of the
ensemble automatically to the information in the data. All of this is done
during a single training run, which is made possible by utilizing developments
in information theory for tree algorithms {\tt arXiv:2008.05926v1 [stat.ME]}.
agtboost also comes with a feature importance function that eliminates the
common practice of inserting noise features. Further, a useful model validation
function performs the Kolmogorov-Smirnov test on the learned distribution.
