Compute the area under the ROC curve of the propensity score.

`computePsAuc(data, confidenceIntervals = FALSE, maxRows = 1e+05)`

- data
A data frame with at least the two columns described below

- confidenceIntervals
Compute 95 percent confidence intervals (computationally expensive for large data sets)

- maxRows
Maximum number of rows to use. If the number of rows is larger, a random sample will be taken. This can increase speed, with minor cost to precision. Set to 0 to use all data.

A tibble holding the AUC and its 95 percent confidence interval

The data frame should have a least the following two columns:

treatment (integer): Column indicating whether the person is in the target (1) or comparator (0) group.

propensityScore (numeric): Propensity score.

```
treatment <- rep(0:1, each = 100)
propensityScore <- c(rnorm(100, mean = 0.4, sd = 0.25), rnorm(100, mean = 0.6, sd = 0.25))
data <- data.frame(treatment = treatment, propensityScore = propensityScore)
data <- data[data$propensityScore > 0 & data$propensityScore < 1, ]
computePsAuc(data)
#> [1] 0.6727075
```