
<!-- README.md is generated from README.Rmd. Please edit that file -->

# picR

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Computation of predictive information criteria (PIC) from select model
object classes for model selection in predictive contexts. In contrast
to the more widely used Akaike Information Criterion (AIC), which are
derived under the assumption that target(s) of prediction
(i.e. validation data) are independently and identically distributed to
the fitting data, the PIC are derived under less restrictive assumptions
and thus generalize AIC to the more practically relevant case of
training/validation data heterogeneity.

## Installation

You can install the development version of picR like so:

``` r
# FILL THIS IN! HOW CAN PEOPLE INSTALL YOUR DEV PACKAGE?
```

## Example

This is a basic example which shows you how to solve a common problem:

``` r
library(picR)
## basic example code
```

What is special about using `README.Rmd` instead of just `README.md`?
You can include R chunks like so:

``` r
summary(cars)
#>      speed           dist       
#>  Min.   : 4.0   Min.   :  2.00  
#>  1st Qu.:12.0   1st Qu.: 26.00  
#>  Median :15.0   Median : 36.00  
#>  Mean   :15.4   Mean   : 42.98  
#>  3rd Qu.:19.0   3rd Qu.: 56.00  
#>  Max.   :25.0   Max.   :120.00
```

You’ll still need to render `README.Rmd` regularly, to keep `README.md`
up-to-date. `devtools::build_readme()` is handy for this. You could also
use GitHub Actions to re-render `README.Rmd` every time you push. An
example workflow can be found here:
<https://github.com/r-lib/actions/tree/v1/examples>.

You can also embed plots, for example:

<img src="man/figures/README-pressure-1.png" width="100%" />

In that case, don’t forget to commit and push the resulting figure
files, so they display on GitHub and CRAN.
