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.