tdigest

The t-Digest construction algorithm, by Dunning et al., (2019) <doi:10.48550/arXiv.1902.04023>, uses a variant of 1-dimensional k-means clustering to produce a very compact data structure that allows accurate estimation of quantiles. This t-Digest data structure can be used to estimate quantiles, compute other rank statistics or even to estimate related measures like trimmed means. The advantage of the t-Digest over previous digests for this purpose is that the t-Digest handles data with full floating point resolution. The accuracy of quantile estimates produced by t-Digests can be orders of magnitude more accurate than those produced by previous digest algorithms. Methods are provided to create and update t-Digests and retrieve quantiles from the accumulated distributions.

Last 30 days

This package has been downloaded 559 times in the last 30 days. Not bad! The download count is somewhere between 'small-town buzz' and 'moderate academic conference'. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 37 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 7,832 times in the last 365 days. A solid achievement! Enough downloads to get noticed at department meetings. The day with the most downloads was Jun 20, 2024 with 96 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.