hal9001

A scalable implementation of the highly adaptive lasso algorithm, including routines for constructing sparse matrices of basis functions of the observed data, as well as a custom implementation of Lasso regression tailored to enhance efficiency when the matrix of predictors is composed exclusively of indicator functions. For ease of use and increased flexibility, the Lasso fitting routines invoke code from the 'glmnet' package by default. The highly adaptive lasso was first formulated and described by MJ van der Laan (2017) doi:10.1515/ijb-2015-0097, with practical demonstrations of its performance given by Benkeser and van der Laan (2016) doi:10.1109/DSAA.2016.93. This implementation of the highly adaptive lasso algorithm was described by Hejazi, Coyle, and van der Laan (2020) doi:10.21105/joss.02526.

Last 30 days

This package has been downloaded 1,255 times in the last 30 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 33 times.

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Last 365 days

This package has been downloaded 14,414 times in the last 365 days. The academic equivalent of having a dedicated subreddit. There are fans, and maybe even a few trolls! The day with the most downloads was Nov 05, 2024 with 105 downloads.

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