---
title: "Introduction to lightAUC"
author: "Christos Adam"
output:
  rmarkdown::html_vignette:
    number_sections: false
  word_document: default
  pdf_document: default
fontsize: 11pt
urlcolor: blue
linkcolor: blue
link-citations: true
header-includes: \usepackage{float}
vignette: >
  %\VignetteIndexEntry{Introduction to lightAUC}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, eval=FALSE)
```

# **Fast AUC computation in R**

Fast and lightweight computation of AUC metric for the binary case (1 positive
and 0 negative) is offered by <b>lightAUC</b> package. The algorithm used is a
fast implementation from algorithm of Fawcett ([2006](#ref-fawcett2006)).

## **Example**

```r
# Create some data
probs   <- c(1, 0.4, 0.8)
actuals <- c(0, 0, 1)
lightAUC(probs, actuals)
```
```
## 0.5
```

For parallel calculations use:
```r
# E.g. 2 cores (you can use cores = parallel::detectCores() for your case)
probs   <- c(1, 0.4, 0.8)
actuals <- c(0, 0, 1)
lightAUC(probs, actuals, parallel = TRUE, cores = 2)
```
```
## 0.5
```

## **References**

<span class="nocase" id="ref-fawcett2006">
Fawcett, T. (2006). An introduction to ROC analysis. <emph>{Pattern Recognition
Letters</emph>, \bold{27}(8), 861–874.
<a href=
"https://doi.org/10.1016/j.patrec.2005.10.010">10.1016/j.patrec.2005.10.010</a>
