NA dropout_curve parameters

If the proDA fit is performed using a subset of rows (n_subsample), and if that subset (the first `n_subsample` rows of the dataset) has no missing values for a particular sample (column), then `dropout_curves()` can't generate parameter estimates, and returns `NA` for the curve coefficients of that sample. When later those dropout curve parameters (including the NA's) are used to predict features for the rest of the dataset, and if/when dropouts are now encounted in the samples having NA-parameter curve coefficients, the `objective_fcn` call seems to go "boom". I'd guess the right "fix" would be to recalc the dropout curve parameters "one last time" after the model has converged, using all the data (at least in this edge case).