Changes in version 2.4
o Added missing protoptypes to some C function definitions.
o Added an argument testInit to the mexhaz function to let the user choose whether several vectors of initial values should be tested in case of failed convergence.
Changes in version 2.3
o Corrected a problem in the formula for the Hessian of the likelihood in the mexhazEgh function.
Changes in version 2.2
o Corrected bugs in the marginSurvhaz, predict.mexhaz, and mexhazEgh functions.
o Added a function to compute hazard ratios and risk ratios.
o Added a function to compute direct adjusted survival estimates.
Changes in version 2.1
o Implemented marginal predictions for models including a random intercept.
o Implemented the correct Wald test for the model coefficients.
o Corrected a problem of scope with the update.mexhaz() function when used inside another function.
Changes in Version 2.0
o For the fixed effect (overall and excess) hazard model as well as for the random effect hazard model (overall only), the default optimisation procedure is Newton-Raphson (from nlm) based on the analytic gradient and Hessian. This greatly improves computational time especially for large datasets. The previous behaviour of the mexhaz function can still be obtained by setting the argument 'exactGradHess' to FALSE.
o The gradient and Hessian of the likelihood evaluated at the estimated parameters are now returned in the mexhaz object.
Changes in Version 1.11
o Added a reference to the JSS article.
o Made a small correction in the update() function.
Changes in Version 1.10
o Corrected a memory issue in the C source code.
Changes in Version 1.9
o Changed the way the Weibull hazard and cumulative hazard rate are
calculated: now, the mexhaz() and predict.mexhaz() functions
directly call a C function, like for the other hazard rates.
o Added a vignette describing the package use based on an article accepted
for publication in JSS.
Changes in Version 1.8
o Fixed a bug in the predict.mexhaz() function that resulted in
missing values for the variances of the logarithm of the hazard and
of the cumulative hazard when confidence intervals are estimated by
Monte Carlo simulations (Thanks Johann!).
o Initial values for the logarithm of the parameters of the Weibull
hazard set to 0.1.
Changes in Version 1.7
o Implemented the correct version of the shared frailty model for left
truncated data.
o Default value of argument 'verbose' set to 0.
o Initial values for the parameters of the Weibull hazard set to 0.1.
Changes in Version 1.6
o Fixed a bug in the mexhaz() function that resulted in an error of
the function when the event variable was 1 for everyone.
o Fixed a bug in the summary() method for objects of class 'mexhaz'.
o Fixed a bug in the update() method for objects of class 'mexhaz'
(provided a default value for the degree of the B-spline).
o Modified the mexhaz() function so that the counting process input
style can be used when fitting a random effect model (stop() changed
into a warning()).
o Added the method lines() for objects of class 'predMexhaz' and
changed the default plotting type for the points() method.
Changes in Version 1.5
o Fixed a bug in the mexhaz() function that resulted in an error of
the function when the covariance matrix could not be estimated. Now,
the function runs until completion and produces a covariance matrix
filled with NA values in case it could not be estimated.
o Added methods fixef() and vcov() for objects of class 'mexhaz'.
o Update of the summary() method for objects of class 'mexhaz' and
addition of a print() methods for objects of class 'summary.mexhaz'.
Changes in Version 1.4
o Correction of a mistake in the predict.mexhaz() function that
resulted in providing the limits of the confidence intervals for
survival when delta.type.s="log" (CI for survival based on a
Wald-type CI for the cumulative hazard) in the wrong order.
o Change in the parametrisation of the Weibull hazard (the
coefficients now correspond to the logarithm of the parameters of
the Weibull distribution, thus allowing unconstrained optimisation).
o Change in the parametrisation of the coefficient related to the
random effect (the coefficient now corresponds to the logarithm of
the standard deviation of the distribution of the random effect,
allowing unconstrained optimisation and consistent with future
multidimensional implementations).