Please see the paper (Kamulete 2022) for details. We denote the R
package as `dsos`

, to avoid confusion with
`D-SOS`

, the method.

To test for adverse shift, we need two main ingredients: outlier
scores from a scoring function and a way to compute a \(p-\)value (or a Bayes factor). First, the
scoring function assigns to a potentially *multivariate*
observation a *univariate* score. Second, for \(p-\)value, we may use permutations. The
prefix *pt* stands for permutation test. The function
`pt_refit`

is a wrapper for this approach, provided we supply
a user-defined scoring function. Sample splitting and out-of-bag
variants are alternatives to permutations. Both use the asymptotic null
distribution and sidestep refitting (recalibrating) the scoring function
after every permutation. As a result, they can be appreciably faster
than inference based on permutations.

Take the `iris`

dataset for example. The training set consists of different flower
species. We use a completely random scoring function `scorer`

for illustration: the outlier scores are drawn from the uniform
distribution.

```
set.seed(12345)
data(iris)
x_train <- iris[1:50, 1:4] # Training sample: Species == 'setosa'
x_test <- iris[51:100, 1:4] # Test sample: Species == 'versicolor'
scorer <- function(tr, te) list(train = runif(nrow(tr)), test = runif(nrow(te)))
iris_test <- pt_refit(x_train, x_test, score = scorer)
plot(iris_test)
```

`dsos`

provides the building blocks for plugging in your
own own scoring functions. The `scorer`

function above is an
example of a custom scoring function. That is, you can bring your own
scores.

Kamulete, Vathy M. 2022. “Test for Non-Negligible Adverse
Shifts.” In *The 38th Conference on Uncertainty in Artificial
Intelligence*. https://openreview.net/forum?id=S5UG2BLi9xc.