Calculates various functions needed for design and monitoring survival trials
accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual,
and different censoring distributions between groups. The event time distribution is assumed to be
piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution.

Calculates various functions needed for design and monitoring survival trials
accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual,
and different censoring distributions between groups. The event time distribution is assumed to be
piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution.
As compared with Version 1.1.0, this version includes the calculation of the restricted mean survival time.

Calculates various functions needed for design and monitoring survival trials
accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual,
and different censoring distributions between groups. The event time distribution is assumed to be
piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution.
As compared with Version 1.1.0, this version includes the calculation of the restricted mean survival time and weighted log-rank statistics.

Calculates various functions needed for design and monitoring survival trials
accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual,
and different censoring distributions between groups. The event time distribution is assumed to be
piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution.
As compared with Version 1.2.1, two more types of hybrid crossover are added.
A bug is corrected in the function "pwecx" that calculates the crossover-adjusted survival, distribution,
density, hazard and cumulative hazard functions.
Also, to generate the crossover-adjusted event time random variable,  a more efficient
algorithm is used and the output includes crossover indicators.