Summarises where missingness sits in an omics layer before a LUCID model is
fitted, so that the choice between listwise and sporadic handling can be made
from the data rather than assumed. Reports missingness by feature and by
subject, flags the features and subjects that are more than half missing, and
counts the distinct missingness patterns present.
The number of distinct patterns is the diagnostic that matters most for cost:
the observed-data likelihood is evaluated once per pattern, so a matrix with
few patterns (largely listwise missingness) is far cheaper to fit than one of
the same sparsity spread over many patterns.