conTree

Contrast trees represent a new approach for assessing the accuracy of many types of machine learning estimates that are not amenable to standard (cross) validation methods; see "Contrast trees and distribution boosting", Jerome H. Friedman (2020) doi:10.1073/pnas.1921562117. In situations where inaccuracies are detected, boosted contrast trees can often improve performance. Functions are provided to to build such trees in addition to a special case, distribution boosting, an assumption free method for estimating the full probability distribution of an outcome variable given any set of joint input predictor variable values.

Last 30 days

This package has been downloaded 177 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 8 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 2,232 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Aug 07, 2024 with 39 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.