depCensoring

Several statistical methods for analyzing survival data under various forms of dependent censoring are implemented in the package. In addition to accounting for dependent censoring, it offers tools to adjust for unmeasured confounding factors. The implemented approaches allow users to estimate the dependency between survival time and dependent censoring time, based solely on observed survival data. For more details on the methods, refer to Deresa and Van Keilegom (2021) doi:10.1093/biomet/asaa095, Czado and Van Keilegom (2023) doi:10.1093/biomet/asac067, Crommen et al. (2024) doi:10.1007/s11749-023-00903-9 and Willems et al. (2024+) doi:10.48550/arXiv.2403.11860.

Last 30 days

This package has been downloaded 309 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 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 1,813 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Oct 20, 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.