## Uses of Interface
org.apache.datasketches.quantilescommon.QuantilesAPI

Packages that use QuantilesAPI
org.apache.datasketches.kll: This package is for the implementations of the sketch algorithm developed by Zohar Karnin, Kevin Lang, and Edo Liberty that is commonly referred to as the "KLL" sketch after the authors' last names.
org.apache.datasketches.quantiles: The quantiles package contains stochastic streaming algorithms that enable single-pass analysis of the distribution of a stream of quantiles.
org.apache.datasketches.quantilescommon: This package contains common tools and methods for the quantiles, kll and req packages.
org.apache.datasketches.req: This package is for the implementation of the Relative Error Quantiles sketch algorithm.

### Uses of QuantilesAPI in org.apache.datasketches.kll

Classes in org.apache.datasketches.kll that implement QuantilesAPI
`class` `KllDoublesSketch` This variation of the KllSketch implements primitive doubles.
`class` `KllFloatsSketch` This variation of the KllSketch implements primitive floats.
`class` `KllItemsSketch<T>` This variation of the KllSketch implements generic data types.
`class` `KllLongsSketch` This variation of the KllSketch implements primitive longs.
`class` `KllSketch` This class is the root of the KLL sketch class hierarchy.

### Uses of QuantilesAPI in org.apache.datasketches.quantiles

Classes in org.apache.datasketches.quantiles that implement QuantilesAPI
`class` `CompactDoublesSketch` Compact sketches are inherently read only.
`class` `DoublesSketch` This is an implementation of the Low Discrepancy Mergeable Quantiles Sketch, using doubles, described in section 3.2 of the journal version of the paper "Mergeable Summaries" by Agarwal, Cormode, Huang, Phillips, Wei, and Yi: Reference: A k of 128 produces a normalized, rank error of about 1.7%.
`class` `ItemsSketch<T>` This is an implementation of the Low Discrepancy Mergeable Quantiles Sketch, using generic items, described in section 3.2 of the journal version of the paper "Mergeable Summaries" by Agarwal, Cormode, Huang, Phillips, Wei, and Yi: Reference: A k of 128 produces a normalized, rank error of about 1.7%.
`class` `UpdateDoublesSketch`

### Uses of QuantilesAPI in org.apache.datasketches.quantilescommon

Subinterfaces of QuantilesAPI in org.apache.datasketches.quantilescommon
`interface` `QuantilesDoublesAPI` The Quantiles API for item type double.
`interface` `QuantilesFloatsAPI` The Quantiles API for item type float.
`interface` `QuantilesGenericAPI<T>` The Quantiles API for item type generic.
`interface` `QuantilesLongsAPI` The Quantiles API for item type long.

Methods in org.apache.datasketches.quantilescommon with parameters of type QuantilesAPI
`static double` KolmogorovSmirnov.`computeKSThreshold(QuantilesAPI sketch1, QuantilesAPI sketch2, double tgtPvalue)` Computes the adjusted delta height threshold for the kolmogorovSmirnovTest(...)
`static boolean` KolmogorovSmirnov.`kolmogorovSmirnovTest(QuantilesAPI sketch1, QuantilesAPI sketch2, double tgtPvalue)` Performs the Kolmogorov-Smirnov Test between two QuantilesAPI sketches.

### Uses of QuantilesAPI in org.apache.datasketches.req

Classes in org.apache.datasketches.req that implement QuantilesAPI
`class` `ReqSketch` This Relative Error Quantiles Sketch is the Java implementation based on the paper "Relative Error Streaming Quantiles" by Graham Cormode, Zohar Karnin, Edo Liberty, Justin Thaler, Pavel Veselý, and loosely derived from a Python prototype written by Pavel Veselý.