In addition to the already existing ones, `adoptr`

allows
the user to implement custom scores. Usually, this will be done by
defining a new sub-class of `ConditionalScore`

. Assume that
one would be interested in the probability of early stopping for
futility. First we create a new class as subclass of
`ConditionalScore`

```
setClass("FutilityStopping", contains = "ConditionalScore")
# constructor
FutilityStopping <- function() new("FutilityStopping")
```

We only need to implement a method `evaluate()`

, all other
methods are inherited from the abstract class
`ConditionalScore`

.

```
setMethod("evaluate", signature("FutilityStopping", "TwoStageDesign"),
function(s, design, x1, optimization = FALSE, ...)
ifelse(x1 < design@c1f, 1, 0)
)
```

The `optimization`

flag here allows to compute scores
differently during the optimization procedure. This is, e.g., used for
the evaluation of conditional power which uses adaptive Gaussian
Quadrature for maximal precision by default but non adaptive Gaussian
Quadrature with the pre-defined integration rule of the design object
during optimization for speed.

The score can now be integrated using the `expected`

method for conditional scores

```
pr_early_futility <- expected(
FutilityStopping(),
Normal(), PointMassPrior(.0, 1)
)
```

and the resulting integral score can be evaluated as usual. Consider again, the design

```
design <- TwoStageDesign(
n1 = 100,
c1f = .0,
c1e = 2.0,
n2_pivots = rep(150, 5),
c2_pivots = sapply(1 + adoptr:::GaussLegendreRule(5)$nodes, function(x) -x + 2)
)
plot(design)
```

Then the value of the expected score is given by

```
evaluate(pr_early_futility, design)
#> [1] 0.5
```

The value is correct since it needs to conform with

```
pnorm(design@c1f)
#> [1] 0.5
```