SMLE

Feature screening is a powerful tool in processing ultrahigh dimensional data. It attempts to screen out most irrelevant features in preparation for a more elaborate analysis. Xu and Chen (2014)[https://doi.org/10.1080/01621459.2013.879531] proposed an effective screening method SMLE, which naturally incorporates the joint effects among features in the screening process. This package provides an efficient implementation of SMLE-screening for high-dimensional linear, logistic, and Poisson models. The package also provides a function for conducting accurate post-screening feature selection based on an iterative hard-thresholding procedure and a user-specified selection criterion.

Last 30 days

This package has been downloaded 647 times in the last 30 days. More downloads than an obscure whitepaper, but not enough to bring down any servers. A solid effort! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 3 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 9,992 times in the last 365 days. A solid achievement! Enough downloads to get noticed at department meetings. The day with the most downloads was Jul 21, 2024 with 153 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.