Changes in version 1.1.23 (2017-07-13) - new function simData.gh() to generate data from Gumbel-Hougaard copula Changes in version 1.1.15 (2017-04-25) - fixed minor issue in prediction function for adjusted copula models Changes in version 1.1.14 (2017-04-06) - fixed issue in prediction intervals for mixed Poisson models Changes in version 1.1.13 (2017-03-28) - fixed issue in prediction intervals for adjusted copula models Changes in version 1.1.12 (2017-03-02) - loocv() now also returns the values of kTau and R2 estimated in each (N-1) fold Changes in version 1.1.10 (2017-02-27) - fixed issues with loocv when few trials (added controls) - added data 'gastadj' - added twoStage parameter to surrosurv for Shih and Louis (1995) approach to copula estimation Changes in version 1.1.4 (2016-12-06) - added paper manuscript as vignette('surrosurv') Changes in version 1.1 (2016-11-09) - Poisson models can be fitted each separately ################################################################################ Changes in version 0.1.1 (2016-09-28) - fixed examples for poissonize() Changes in version 0.1.0 (First Complete version, 2016-09-27) - new function loocv() (with print() and plot() functions) to compute leave-one-out cross-validation ################################################################################ Changes in version 0.0.15 (2016-09-23) - new function ste() to compute the surrogate threshold effect - plot.sussosurv() can now show prediction intervals and the STE Changes in version 0.0.11 (2016-08-12) - predict and plot for class sussosurv Changes in version 0.0.10 (2016-08-01) - kkt2 convergence criteria corrected from positive determinant to positive min eigenvalue Changes in version 0.0.9 (2016-07-28) - bugfix in Poisson method, which did not return results because of mispelled model name Changes in version 0.0.7 (2016-07-25) - the Kendall's tau for copulas is now initialized using the SurvCorr package (much faster) Changes in version 0.0.6 (2016-07-22) - added the function convals(), giving the values of the max abs gradient and the min eigenvalue of the variance-covariance matrix of the random treatment effects - the function convergence() uses explicit computation provided by covals(), instead of using the function optimx in the package optimx