OneStep

Provide principally an eponymic function that numerically computes the Le Cam's one-step estimator for an independent and identically distributed sample. One-step estimation is asymptotically efficient (see L. Le Cam (1956) https://projecteuclid.org/euclid.bsmsp/1200501652) and can be computed faster than the maximum likelihood estimator for large observation samples, see e.g. Brouste et al. (2021) doi:10.32614/RJ-2021-044.

Last 30 days

This package has been downloaded 208 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 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 3,285 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 Jul 21, 2024 with 71 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.