## Conditional Predictive Impact

David S. Watson, Marvin N. Wright

### Introduction

The conditional predictive impact (CPI) is a measure of conditional independence. It can be calculated using any supervised learning algorithm, loss function, and knockoff sampler. We provide statistical inference procedures for the CPI without parametric assumptions or sparsity constraints. The method works with continuous and categorical data.

### Installation

To install the ranger R package from CRAN, just run

`install.packages("cpi")`

To install the development version from GitHub using `devtools`

, run

`devtools::install_github("bips-hb/cpi")`