                        Changes in version 1.4.1                        

  - Fixed saturated-frequency handling for partially missing data when
    missing patterns map to candidate observed cells of equal length.
  - Updated internal response encoding to use -1 for missing values and
    zero-based category indices for C++ calculations.
  - Kept simulated manifest responses as plain factor data frames
    without internal encoding attributes.
  - Fixed predict.slcafit() when newdata is omitted.
  - Added validation for predict() inputs, simulation levels, and
    initial parameter lengths.
  - Added validation for model formulas, regression latent outcomes,
    bootstrap counts, and simulation counts.
  - Fixed simulated factor responses when some generated categories are
    absent in a sample.
  - Fixed Hessian-based covariance extraction and regression confidence
    interval returns.
  - Fixed regression standard errors and Wald p-values in
    summary.reg.slca().
  - Hardened gof() list calls and bias-adjusted regression internals.
  - Recorded bootstrap replicate failures in model-fit diagnostics
    instead of returning error objects.
  - Clarified the documented structure of convergence, predict(), and
    simulated responses.
