To assess and compare the models' goodness of fit, R-squared is one of the most 
              popular measures. For categorical data analysis, however, no universally adopted 
              R-squared measure can resemble the ordinary least square (OLS) R-squared for 
              linear models with continuous data. This package implement the surrogate R-squared
              measure for categorical data analysis, which is proposed in the study of Dungang Liu,
              Xiaorui Zhu, Brandon Greenwell, and Zewei Lin (2022) <doi:10.1111/bmsp.12289>. It
              can generate a point or interval measure of the surrogate R-squared. It can also 
              provide a ranking measure of the percentage contribution of each variable to the 
              overall surrogate R-squared. This ranking assessment allows one to check the 
              importance of each variable in terms of their explained variance. This package can
              be jointly used with other existing R packages for variable selection and model 
              diagnostics in the model-building process.

Provides functions to test for a treatment effect in terms of the difference in survival between a treatment group and a control group using surrogate marker information obtained at some early time point in a time-to-event outcome setting. Nonparametric kernel estimation is used to estimate the test statistic and perturbation resampling is used for variance estimation. More details will be available in the future in: Parast L, Cai T, Tian L (2019) ``Using a Surrogate Marker for Early Testing of a Treatment Effect" Biometrics, 75(4):1253-1263. <doi:10.1111/biom.13067>.

Provides functions for the evaluation of
    surrogate endpoints when both the surrogate and the true endpoint are failure
    time variables. The approaches implemented are:
    (1) the two-step approach (Burzykowski et al, 2001) <DOI:10.1111/1467-9876.00244> with a copula model (Clayton, Plackett, Hougaard) at
    the first step and either a linear regression of log-hazard ratios at the second
    step (either adjusted or not for measurement error);
    (2) mixed proportional hazard models estimated via mixed Poisson GLM
    (Rotolo et al, 2017 <DOI:10.1177/0962280217718582>).