Generate a cluster assignment from a CTS, SRS, or ASRS similarity matrix.

`LCE(E, k, dc = 0.8, R = 10, sim.mat = c("cts", "srs", "asrs"))`

- E
is an array of clustering results. An error is thrown if there are missing values.

`impute_missing()`

can be used beforehand.- k
requested number of clusters

- dc
decay constant for CTS, SRS, or ASRS matrix

- R
number of repetitions for SRS matrix

- sim.mat
similarity matrix; choices are "cts", "srs", "asrs".

a vector containing the cluster assignment from either the CTS, SRS, or ASRS similarity matrices

Other consensus functions:
`CSPA()`

,
`LCA()`

,
`k_modes()`

,
`majority_voting()`

```
data(hgsc)
dat <- hgsc[1:100, 1:50]
x <- consensus_cluster(dat, nk = 4, reps = 4, algorithms = c("km", "hc"),
progress = FALSE)
if (FALSE) {
LCE(E = x, k = 4, sim.mat = "asrs")
}
x <- apply(x, 2:4, impute_knn, data = dat, seed = 1)
x_imputed <- impute_missing(x, dat, nk = 4)
LCE(E = x_imputed, k = 4, sim.mat = "cts")
#> [1] 1 1 1 1 1 1 1 1 1 2 1 1 2 1 3 1 1 2 1 1 2 1 2 1 1 1 2 1 2 1 1 1 1 1 2 2 1
#> [38] 1 4 1 2 1 1 1 1 1 1 1 1 2 1 1 2 2 2 1 1 1 1 1 2 2 1 1 2 2 2 1 1 2 1 2 1 1
#> [75] 1 1 1 1 4 2 1 1 1 1 1 1 1 1 3 1 1 1 2 1 1 2 2 1 2 1
```