Inference procedures accommodate a flexible range of hazard ratio patterns with a two-sample semi-parametric model. This model contains the proportional hazards model and the proportional odds model as sub-models, and accommodates non-proportional hazards situations to the extreme of having crossing hazards and crossing survivor functions. Overall, this package has four major functions: 1) the parameter estimation, namely short-term and long-term hazard ratio parameters; 2) 95 percent and 90 percent point-wise confidence intervals and simultaneous confidence bands for the hazard ratio function; 3) p-value of the adaptive weighted log-rank test; 4) p-values of two lack-of-fit tests for the model. See the included "read_me_first.pdf" for brief instructions. In this version (1.1), there is no need to sort the data before applying this package.

Provides covariate-adjusted comparison of two groups of right 
    censored data, where the binary group variable has separate short-term 
    and long-term effects on the hazard function, while effects of covariates 
    such as age, blood pressure, etc. are proportional on the hazard. The model 
    was studied in Yang and Prentice (2015) <doi:10.1002/sim.6453> and it extends 
    the two sample version of the short-term and long-term hazard ratio model
    proposed in Yang and Prentice (2005) <doi:10.1093/biomet/92.1.1>. The model 
    extends the usual Cox proportional hazards model to allow more flexible 
    hazard ratio patterns, such as gradual onset of effect, diminishing effect, 
    and crossing hazard or survival functions. This package provides 
    the following: 1) point estimates and confidence intervals for model 
    parameters; 2) point estimate and confidence interval of the average hazard 
    ratio; and 3) plots of estimated hazard ratio function with point-wise and 
    simultaneous confidence bands.

Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with piecewise exponential baseline distribution. Details about the model can be found in Demarqui and Mayrink (2019) <arXiv:1910.02406>. Model fitting carried out via likelihood-based and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times.