lpo_auc: Compute the leave-pair-out cross-validation estimator of AUC.
In benkeser/predtmle: Small sample estimators of cross-validated prediction metrics

This estimator is computed by leaving out a pair of one case (Y = 1) and one control (Y = 0). The learner is trained on the remaining observations and predicted values are obtained for the left-out pair. The estimate is given by the proportion of left-out pairs for which the case had higher predicted risk than the control.

1 2	lpo_auc(Y, X, learner = "glm_wrapper", max_pairs = NULL, parallel = FALSE, ...)

`Y`	A numeric vector of outcomes, assume to equal `0` or `1`.
`X`	A `data.frame` of variables for prediction.
`learner`	A wrapper that implements the desired method for building a prediction algorithm. See TODO: ADD DOCUMENTATION FOR WRITING
`max_pairs`	The maximum number of pairs to leave out.
`parallel`	A boolean indicating whether prediction algorithms should be trained in parallel. Default to `FALSE`.
`...`	Other options (not currently used)

# simulate data
X <- data.frame(x1 = rnorm(50))
Y <- rbinom(50, 1, plogis(X$x1))
# compute lpo_auc for logistic regression
lpo <- lpo_auc(Y = Y, X = X, learner = "glm_wrapper")