Changes in version 0.0.1.4 (2025-03-17)                 

Changes

  - Updated the names of objects in the output to improve clarity and
    consistency.
  - The following output objects have been renamed:
      - p_dual → probabilities
      - e_dual → errors
  - Added q to output$values to facilitate verifications and debugging.

Bug Fixes

  - Fixed a bug in 'ei_gce()' where the 'q' argument always took the
    default value when the user provided a vector ignoring the input
    entered.
  - This issue did not affect cases where 'q' was a matrix ('matrix()'),
    which were already handled correctly.

                Changes in version 0.0.1.3 (2024-11-04)                 

Bug Fixes

-Fixed an issue with the weights argument where, in some cases, custom
weight columns were not recognized, causing equal weights to be assigned
to all observations. Now, users can specify any column name as a string
for weights in datahp and datahs, with the function correctly applying
the specified weights for improved flexibility and usability.

                Changes in version 0.0.1.2 (2024-11-02)                 

Main changes

This update introduces significant improvements in the flexibility,
accuracy, and functionality of the package. Below are the primary
modifications:

  - Correction in handling the dependent variable (y): An error in
    processing the dependent variable led to inaccuracies in
    estimations. This issue has been resolved, ensuring reliable and
    consistent results.

  - Extended compatibility for the y variable: The y variable can now be
    a categorical variable with multiple levels (J > 2). When y is a
    dummy variable, the first column represents level 1. For categorical
    variables, levels are ordered alphabetically.

  - Correction in handling the error prior (l): Previously, the value l
    did not include index 3. This version corrects that omission.

  - Enhancements in q assignment: q can now be specified as a vector
    rather than only a single value per category. The function now
    correctly handles uniform distributions regardless of the number of
    categories.

  - New default tolerance level: The function tolerance is set to 1e-10
    for improved optimization accuracy.

  - Change in optimization method to nlminb: This version uses nlminb
    instead of optim, improving results and eliminating the need for the
    method argument.