SurvMA

Provide model averaging-based approaches that can be used to predict personalized survival probabilities. The key underlying idea is to approximate the conditional survival function using a weighted average of multiple candidate models. Two scenarios of candidate models are allowed: (Scenario 1) partial linear Cox model and (Scenario 2) time-varying coefficient Cox model. A reference of the underlying methods is Li and Wang (2023) doi:10.1016/j.csda.2023.107759.

Last 30 days

This package has been downloaded 353 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 2 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 3,059 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 Jan 24, 2025 with 59 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.