mboost

Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. Models and algorithms are described in doi:10.1214/07-STS242, a hands-on tutorial is available from doi:10.1007/s00180-012-0382-5. The package allows user-specified loss functions and base-learners.

Last 30 days

This package has been downloaded 4,264 times in the last 30 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 137 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 56,766 times in the last 365 days. An impressive feat! Enough downloads to make even seasoned academics take note. The day with the most downloads was Aug 22, 2024 with 429 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.