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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.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.loglik_*
and gradlik_*
) for enhanced
numerical stability and clarity.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.sigma
and rho
parameters.HeckmanCL()
and
other core functions.HeckmanCL()
) and foundational sample selection
models.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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