THIS IS THE CHANGELOG OF THE "maxLik" 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 1.2-4 (2014-12-31)
* Equality constraints (SUMT) checks conformity of the matrices
* coef.maxim() is now exported
* added argument "digits" to print.summary.maxLik()
* added argument "digits" to condiNumber.default()
* further arguments to condiNumber.maxLik() are now passed to
condiNumber.default() rather than to hessian()
CHANGES IN VERSION 1.2-0 (2013-10-22)
* Inequality constraints now support multiple constraints
(B may be a vector).
* Fixed a bug in documentation, inequality constraint requires
A %*% theta + B > 0, not >= 0 as stated earlier.
* function sumKeepAttr() is imported from the miscTools package now (before
maxLik() could not be used by another package when this package imported (and
not depended on) the maxLik package) (bug reported and solution provided by
Martin Becker)
CHANGES IN VERSION 1.1-8 (2013-09-17)
* fixed bug that could occur in the Newton-Raphson algorithm if the
log-likelihood function returns a vector with observation-specific values
or if there are NAs in the function values, gradients, or Hessian
CHANGES IN VERSION 1.1-4 (2013-09-16)
* the package code is byte-compiled
* if the log-likelihood function contains NA, the gradient is not calculated;
if components of the gradient contain NA, the Hessian is not calculated
* slightly improved documentation
* improved warning messages and error messages when doing constrained
optimisation
* added citation information
* added start-up message
CHANGES IN VERSION 1.1-2 (2012-03-04)
* BHHH only considers free parameters when analysing the size of gradient
* numericGradient and numericHessian check for the length of
vector function
CHANGES IN VERSION 1.1-0 (2012-01-...)
* Conjugate-gradient (CG) optimization method included.
* it is guaranteed now that the variance covariance matrix returned
by the vcov() method is always symmetric.
* summary.maxLik is guaranteed to use maxLik specific methods, even if
corresponding methods for derived classes have higher priority.
CHANGES IN VERSION 1.0-2 (2011-10-16)
This is mainly bugfix release.
* maxBFGSR works with fixed parameters.
* maxBFGS and other optim-based routines work with both fixed
parameters and inequality constraints.
* constrOptim2 removed from API. Names of it's formal arguments are
changed.
CHANGES IN VERSION 1.0-0 (2010-10-15)
* moved the generic function stdEr() including a default method and a method
for objects of class "lm" to the "miscTools" package (hence, this package now
depends on the version 0.6-8 of the "miscTools" package that includes stdEr()
* if argument print.level is 0 (the default) and some parameters are
automatically fixed during the estimation, because the returned log-likelihood
value has attributes "constPar" and "newVal", the adjusted "starting values"
are no longer printed.
CHANGES IN VERSION 0.8-0
* fixed bug that occured in maxBFGS(), mxNM(), and maxSANN if the model had only
one parameter and the function specified by argument "grad" returned a vector
with the analytical gradients at each observation
* maxNR() now performs correctly with argument "iterlim" set to 0
* maxNR, maxBHHH(), maxBFGS(), maxNM(), and maxSANN() now use attributes
"gradient" and "hessian" of the object returned by the log-likelihood function;
if supplied, these are used instead of arguments "grad" and "hess"
* added function maxBFGSR() that implements the BFGS algorithm (in R); this
function was originally developed by Yves Croissant and placed in the "mlogit"
package
* maxNR() now has an argument "bhhhHessian" (defaults to FALSE): if this
argument is TRUE, the Hessian is approximated by the BHHH method (using
information equality), i.e. the BHHH optimization algorithm is used
* maxLik() now has an argument 'finalHessian'; if it is TRUE, the final
Hessian is returned; if it is the character string "BHHH", the BHHH
approximation of the Hessian matrix (using information equality) with attribute
"type" set to "BHHH" is returned
* maxNR(), maxBHHH(), maxBFGS(), maxNM(), and maxSANN() now additionally return
a component "gradientObs" that is the matrix of gradients evaluated at each
observation if argument "grad" returns a matrix or argument "grad" is not
specified and argument "fn" returns a vector
* the definitions of the generic functions nObs() and nParam() have been moved
to the "miscTools" package
* added methods bread() and estfun() for objects of class "maxLik" (see
documentation of the generic functions bread() and estfun() defined in package
"sandwich")
* replaced argument "activePar" of numericGradient(), numericHessian(), and
numericNHessian() by argument "fixed" to be consistent with maxLik(), maxNR(),
and the other maxXXX() functions
* maxNR(), maxBHHH(), maxBFGSYC(), maxBFGS(), maxNM(), maxSANN(), and
summary.maxLik() now return component "fixed" instead of component "activePar"
CHANGES IN VERSION 0.7-2
* corrected negative definiteness correction of Hessian in maxNR() which led
to infinite loops
* changed stopping condition in sumt(): instead of checking whether estimates
are stimilar, we check for penalty being low now
CHANGES IN VERSION 0.7-0
* Holding parameters fixed in maxNR() (and hence, also in maxBHHH()) should
now be done by the new (optional) argument "fixed", because it is convenient
to use than the "old" argument "activePar" in many situations. However, the
"old" argument "activePar" is kept for backward-compatibility.
* added (optional) argument "fixed" to functions maxBFGS(), maxNM(), and maxSANN(),
which can be used for holding parameters fixed at their starting values
* added function constrOptim2(), which is a modified copy of constrOptim()
from the "stats" package, but which includes a bug fix
* added optional argument "cand" to function maxSANN(), which can be used to
specify a function for generating a new candidate point (passed to argument
"gr" of optim())
* added argument "random.seed" to maxSANN() to ensure replicability
* several mainly smaller improvements in ML estimations with linear equality
and inequality constraints (via sumt() and constrOptim2(), respectively)
* several internal changes that make the code easier to maintain
CHANGES IN VERSION 0.6-0
* maxLik() can perform maximum likelihood estimations under linear equality
and inequality constraints on the parameters now (see documentation of the
new argument "constraints"). Please note that estimations under constraints
are experimental and have not been thoroughly tested yet.
* a new method "stdEr" to extract standard errors of the estimates has been
introduced
* added a "coef" method for objects of class "summary.maxLik" that extracts
the matrix of the estimates, standard errors, t-values, and P-values
* some minor bugs have been fixed
* we did some general polishing of the returned object and under the hood
CHANGES IN VERSION 0.5-12 AND BEFORE
* please take a look at the log messages of the SVN repository on R-Forge