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...there are some news. The most noteworthy are: (i) 'plot.Surv' has a few new options, (ii) 'weibreg' now can fit null models (i.e., only scale and shape) and covariates are no longer automatically centered (although this is still the default), (iii) there is a new function, 'toBinary', which transforms a data frame suitable for survival analysis to a data frame suitable for ,eg, logistic regression as a substitute for a discrete time cox regression. In the process, sampling of survivors in risk sets is possible. G?ran -- G?ran Brostr?m, professor phone: 46 90 7...

For time scale that are truly discrete Cox proposed the "exact partial likelihood". I call that the "exact" method and SAS calls it the "discrete" method. What we compute is precisely the same, however they use a clever algorithm which is faster. To make things even more confusing, Prentice introduced an "exact marginal likelihood" which is not