Known Limitations

• transform(new_data) is not implemented. Only training scores are returned. To project new data, refit on the combined dataset.

• inverse_transform() always returns dense output, even when the input was sparse CSR/CSC.

• MissingAwareSparseOneHotEncoder requires numeric categories. String categories cannot survive the CSR round-trip.

• Data convention. AutoEncoder expects samples × features; VBPCA expects features × samples. Transpose as needed.

• RMS oscillation with uncentered data. When bias=True (the default) and the input data has non-zero feature means, the RMS convergence trace can exhibit a stable period-2 oscillation caused by a one-iteration lag between the mean update and the reconstruction error.

  Workaround: center your data before fitting — use MissingAwareStandardScaler (or AutoEncoder) as a preprocessing step. Pre-centered data eliminates the oscillation entirely, even with bias=True.

• Fits are non-reproducible unless seeded. VBPCA(random_state=None) (the default) draws fresh entropy for parameter initialization and any auto-generated xprobe mask on every call to fit(), so repeated fits on the same data can converge to different results. Pass an int or np.random.Generator via random_state for reproducible runs. Prior to #109, the default initialization was silently seeded with a fixed value; this is no longer the case.