README.Rmd

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output: github_document
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# Overview

`predtools` provides miscellaneous tools for developing and evaluating prediction models.

# Table of Contents

-   [Installation](#installation)
-   [Example](#example)
-   [Model-based ROC](https://resplab.github.io/predtools/articles/mROC.html)
-   [Intercept Adjustment](https://resplab.github.io/predtools/articles/interceptAdj.html)
-   [Calibration Plot](https://resplab.github.io/predtools/articles/calibPlot.html)
-   [Unit Normal Loss Integral in Two Dimensions](https://resplab.github.io/predtools/articles/UNLI2D.html)

## Installation {#installation}

You can install the released version of predtools from [CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("predtools")
```

And the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("remotes")
remotes::install_github("resplab/predtools")
```

## Example {#example}

The function `calibration_plot` takes observed and predicted values from a prediction model and uses ggplot2 to produce a calibration plot:

    library(predtools)
    library(dplyr)
    x <- rnorm(100, 10, 2)
    y <- x + rnorm(100,0, 1)
    data <- tibble(x,y)
    calibration_plot(data, obs = "x", pred_1 = "y")

See vignettes for more advanced functionalities, including [model-based ROC](https://resplab.github.io/predtools/articles/mROC.html), [intercept adjustment](https://resplab.github.io/predtools/articles/interceptAdj.html), [calibration plot](https://resplab.github.io/predtools/articles/calibPlot.html), and [unit normal loss integral in two dimensions](https://resplab.github.io/predtools/articles/UNLI2D.html)

You can also access the vignettes from R:

    browseVignettes("predtools")