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---
output: rmarkdown::github_document
editor_options:
chunk_output_type: inline
---
```{r pkg-knitr-opts, include=FALSE}
knitr::opts_chunk$set(collapse=TRUE, fig.retina=2, message=FALSE, warning=FALSE)
options(width=120)
```
[![Travis-CI Build Status](https://travis-ci.org/hrbrmstr/tdigest.svg?branch=master)](https://travis-ci.org/hrbrmstr/tdigest)
[![Coverage Status](https://codecov.io/gh/hrbrmstr/tdigest/branch/master/graph/badge.svg)](https://codecov.io/gh/hrbrmstr/tdigest)
[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/tdigest)](https://cran.r-project.org/package=tdigest)
# tdigest
Accurate Quantiles Using 't-Digests'
## Description
The t-digest construction algorithm 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. With small changes, the t-digest can handle values from any ordered set for which we can compute something akin to a mean. The accuracy of quantile estimates produced by t-digests can be orders of magnitude more accurate than those produced by previous digest algorithms.
## What's Inside The Tin
The following functions are implemented:
## Installation
```{r install-ex, eval=FALSE}
devtools::install_git("https://sr.ht.com/~hrbrmstr/tdigest.git")
# or
devtools::install_git("https://gitlab.com/hrbrmstr/tdigest.git")
# or (if you must)
devtools::install_github("hrbrmstr/tdigest")
```
## Usage
```{r lib-ex}
library(tdigest)
# current version
packageVersion("tdigest")
```
## tdigest Metrics
```{r cloc, echo=FALSE}
cloc::cloc_pkg_md()
```
## Code of Conduct
Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md).
By participating in this project you agree to abide by its terms.