ssmodels 2.0.1

Bug Fixes

• Corrected the initialization of the start values in the HeckmanSK function. Previously, it was relying on a two-step method to generate starting values, which could lead to numerical instability in some cases. Now, a more robust initialization is implemented to ensure better convergence and numerical stability.
• Fixed the display of the log-likelihood in the summary methods of all functions (e.g., summary.HeckmanSK, summary.HeckmanCL, summary.HeckmanBS, etc.). Previously, these were reporting the negative of the log-likelihood. They now correctly display the log-likelihood value as returned by the optimization procedure.

ssmodels 2.0.0

Major updates

• Complete overhaul of the package, improving organization, readability, and performance of all functions.
• Rewritten log-likelihood and gradient functions (loglik_* and gradlik_*) for enhanced numerical stability and clarity.
• Fixed discrepancies where analytical gradients did not match numerical gradients.
• Comprehensive documentation updates for all functions, ensuring better understanding and usage.
• Added two new helper functions:
  • postprocess_theta(): streamlines parameter transformations for clear interpretation and improved consistency across models.
  • extract_model_components(): extracts model.frame, model.matrix, and model.response objects in a robust and reusable way.
• All functions now follow consistent coding style and best practices.
• Significant performance improvements, making the package lighter and more efficient.

Bug fixes

• Fixed issues with incorrect gradient calculations for sigma and rho parameters.
• Corrected numerical errors in several model functions.

Other improvements

• Updated vignette and examples to reflect the new structure and improvements.
• Switched pkgdown site to Bootstrap 5 for improved readability and responsiveness.

ssmodels 1.0.1

Minor updates

• Improved documentation and examples.
• Added unit tests to ensure stability of HeckmanCL() and other core functions.

ssmodels 1.0.0

Initial release

• Initial implementation of the classic Heckman model (HeckmanCL()) and foundational sample selection models.
• Basic infrastructure for selection bias correction in econometric models.