fitpgam() and related functions added to fit a log-additive model over two
time scales.fitvcm() and related functions added to fit a varying-coefficient model over
two time scales.select_model2ts() added to compare several fitted models and identify the
best-fitting one.make_grid() facilitates the creation of a new plotting grid.prepare_data(), fit1ts(), and fit2ts().predict.haz2ts() can now predict also including covariates fixed at arbitrary
values
GLAM_2d_covariates() returns covariances between the alpha and beta parameters
All functions that use ucminf to minimize the AIC/BIC of the model wrt the smoothing
parameter(s) now have a smaller value for the option xtol.
Additionally, it can also be changed in the control lists.
Fixed a small typo in plot_haz1ts() that did not allow to plot confidence intervals in
color specified by user.
Fixed problem with variable names in prepare_data_LMMsolver()
print.data2ts() now prints rounded values for total exposure time
predict.haz2ts() method added to objects of class 'haz2ts'.
It allows prediction of the hazard, its standard errors, the cumulative hazard
and the survival probability, from a fitted model of type 'haz2ts', for
arbitrary values of the time scales. It can also be used to obtain individual
predictions for the original data points.
predict_comprisk2ts() allows prediction for competing risks models.
It takes as input a list of cause-specific hazard models over two time scales,
all fitted with fit2ts(), on the same grid, and a new data.frame with values
of the two time scales for which predictions are requested.
DESCRIPTION has been updated, to correct a small typo.