THIS IS THE CHANGELOG OF THE "sampleSelection" PACKAGE
Please note that only the most significant changes are reported here.
A full ChangeLog is available in the log messages of the SVN repository
on R-Forge.
CHANGES IN VERSION 1.0-4 (2015-11-03)
* fixed bug in selection( ..., method = "model.frame" ) for Tobit-5 models
(missing "drop = FALSE") that caused model.frame() to return an incorrect
variable name if the model is estimated by the ML method and all explanatory
variables of the second equation of the outcome model are already included in
the first equation of the outcome model; the incorrect variable name caused
model.matrix(), residuals(), fitted(), and predict() to fail
* The R packages "mvtnorm" and "VGAM" are now 'imported' rather than 'suggested'
* minor adjustments due to the upgrade of the maxLik package to version 1.3
* several minor improvements that are mostly not visible to users
CHANGES IN VERSION 1.0-2 (2014-06-24)
* changed/simplified a few examples in order to reduce their execution time
CHANGES IN VERSION 1.0-0 (2014-06-24)
* bug fix: selection() did not pass some arguments to other functions, e.g.
argument "inst" was ignored so that the second step of heckit-2 models was
estimated by OLS rather than by IV estimation although argument "inst" was
specified
* fixed bug in residuals.selection( , part = "selection" ) that occured when
the dependent variable of the selection model was a factor
* fixed bug that made residuals( object, part = "outcome" ) not work if
"object" was a sample selection model with a constructed variable
(e.g. "log(wage)") estimated by ML
* residuals.selection( , part = "outcome" ) now works for models with binary
dependent outcome variables
* probit models, tobit-2 models, and binary selection models can now be
estimated with observation-specific weights
* in case of a binary dependent variable of the outcome equation,
fitted( object, part = "outcome" ) now returns the probabilities rather than
the linear prdictors
* added a predict() method for probit models
* added a logLik() method for probit models
* added nobs() and nObs() methods for probit and selection models
* added argument "maxMethod" to heckit2fit() and heckit5fit() that specifies
the maximisation method that is used for estimating the probit model (1st stage)
* the default df.residual() method works for probit models
* this package no longer depends on package 'systemfit' but it imports function
systemfit from package "systemfit"
* minor adjustments in invMillsRatio() so that it works with bivariate
probit models estimated by the current version of the VGAM package (0.9-4)
* improved documentation
* modified some internal calculations so that they are numerically nore stable
* removed the deprecated function tobit2()
CHANGES IN VERSION 0.7-2 (2012-08-14)
* fixed a bug in the calculation of the Hessian of the log likelihood function
for probit models that only occurred in specific circumstances
(reported by Jon K. Peck)
* in Tobit 5 models, argument "outcome" can now be a pre-defined list
(bug reported by Jon K. Peck)
CHANGES IN VERSION 0.7-0 (2012-03-04)
* Binary-outcome selection models
CHANGES IN VERSION 0.6-12 (2011-11-13)
* fixed a bug that occurred in the fitted( ..., part = "outcome" ),
model.matrix( ..., part = "outcome" ), and model.frame() methods as well as
in selection( ..., method = "model.frame" ) if all regressors of the outcome
equation are already included in the selection equation. Thanks to TimothÃ©e
Carayol for reporting this bug!
* fixed partial argument matching in invMillsRatio() and probit()
CHANGES IN VERSION 0.6-10
* added a NAMESPACE file
* minor modifications so that the sampleSelection package works with the
versions >= 0.7-3 of the maxLik package
CHANGES IN VERSION 0.6-8 AND BEFORE
* please take a look at the log messages of the SVN repository on R-Forge