https://github.com/gbrsales/kll
Streaming Quantile Approximation for R
https://github.com/gbrsales/kll
r statistics stream-processing
Last synced: about 1 year ago
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Streaming Quantile Approximation for R
- Host: GitHub
- URL: https://github.com/gbrsales/kll
- Owner: gbrsales
- Created: 2018-10-27T17:47:05.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-10-30T15:54:44.000Z (over 7 years ago)
- Last Synced: 2025-02-06T22:42:11.063Z (over 1 year ago)
- Topics: r, statistics, stream-processing
- Language: R
- Size: 14.6 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# kll
An R package implementing the first algorithm described by Karnin, Lang and
Liberty in [Optimal Quantile Approximation in Streams](http://arxiv.org/abs/1603.05346).
Efficiently computes (an approximation of) the CDF of numeric values stored in a vector or in a [DelayedArray](https://bioconductor.org/packages/release/bioc/html/DelayedArray.html).
## Usage
```{r}
library(DelayedArray)
library(kll)
d <- DelayedArray(array(runif(1000000, dim = c(1000000, 1))))
approx_cdf(d, 20L)
```
The library handles blocking transparently. For instance, the code below will process the array in chunks of 100 rows each while producing the same final result as above.
```{r}
setAutoGridMaker(function(x) rowGrid(x, nrow = 100))
approx_cdf(d, 20L)
```
It is also possible to obtain column-level CDFs:
```{r}
d <- DelayedArray(array(runif(1000), dim = c(500, 2)))
approx_col_cdf(d, 20L)
```
## Stability
The package is still under active development. It should be considered **experimental**.