Given a consensus matrix, returns the proportion of ambiguous clusters (PAC). This is a robust way to assess clustering performance.

`PAC(cm, lower = 0, upper = 1)`

- cm
consensus matrix. Should be symmetric and values between 0 and 1.

- lower
the lower bound that determines what is ambiguous

- upper
the upper bound that determines what is ambiguous

the PAC is a score used in clustering performance. The lower it is the better, because we want minimal ambiguity amongst the consensus.

Since a consensus matrix is symmetric, we only look at its lower (or upper)
triangular matrix. The proportion of entries strictly between `lower`

and
`upper`

is the PAC. In a perfect clustering, the consensus matrix would
consist of only 0s and 1s, and the PAC assessed on the (0, 1) interval would
have a perfect score of 0. Using a (0.1, 0.9) interval for defining ambiguity
is common as well.

The PAC is not, strictly speaking, an internal validity index. Originally used to choose the optimal number of clusters, here we use it to assess cluster stability. However, PAC is still agnostic any gold standard clustering result so we use it like an internal validity index.

Senbabaoglu, Y., Michailidis, G., & Li, J. Z. (2014). Critical limitations of consensus clustering in class discovery. Scientific reports, 4.

```
set.seed(1)
x <- replicate(100, rbinom(100, 4, 0.2))
y <- consensus_matrix(x)
PAC(y, lower = 0.05, upper = 0.95)
#> [1] 1
```