Wicked Fast, Accurate Quantiles Using t-Digests
Serialize a tdigest object to an R list or unserialize a serialized td...
Pipe operator
Add a value to the t-Digest with the specified count
Allocate a new histogram
Merge one t-Digest into another
Return the quantile of the value
Total items contained in the t-Digest
Return the value at the specified quantile
Create a new t-Digest histogram from a vector
Calculate sample quantiles from a t-Digest
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.