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>).

Nonparametric and semiparametric estimations of the time-dependent ROC curve for an incomplete failure time data with surrogate failure time endpoints.

Fit Cox non-proportional hazards models with time-varying coefficients. 
    Both unpenalized procedures (Newton and proximal Newton) and penalized procedures 
    (P-splines and smoothing splines) are included using B-spline basis functions for 
    estimating time-varying coefficients. For penalized procedures, cross validations, 
    mAIC, TIC or GIC are implemented to select tuning parameters. Utilities for 
    carrying out post-estimation visualization, summarization, point-wise confidence 
    interval and hypothesis testing are also provided.
    For more information, see Wu et al. (2022) <doi: 10.1007/s10985-021-09544-2> and 
    Luo et al. (2023) <doi:10.1177/09622802231181471>.

Performs inference of several model-free group contrast measures, which include difference/ratio of cumulative incidence rates at given time points, quantiles, and restricted mean survival times (RMST). Two kinds of covariate adjustment procedures (i.e., regression and augmentation) for inference of the metrics based on RMST are also included.

Performs two-sample comparisons based on average hazard with survival weight (AHSW) or general censoring-free incidence rate (CFIR) proposed by Uno and Horiguchi (2023) <doi:10.1002/sim.9651>.

A modular toolkit for interpretable survival machine learning
  with a unified interface for fitting, prediction, evaluation, and
  interpretation.
  It includes semiparametric, parametric, tree-based, ensemble, boosting,
  kernel, and deep-learning survival learners, together with benchmarking,
  scoring, calibration, and model-agnostic interpretation utilities.
  Representative methodological anchors include Cox (1972)
  <doi:10.1111/j.2517-6161.1972.tb00899.x>, Royston and Parmar (2002)
  <doi:10.1002/sim.1203>, Ishwaran et al. (2008) <doi:10.1214/08-AOAS169>,
  Jaeger et al. (2019) <doi:10.1214/19-AOAS1261>, Harrell et al. (1982)
  <doi:10.1001/jama.1982.03320430047030>, Graf et al. (1999)
  <doi:10.1002/(SICI)1097-0258(19990915/30)18:17/18%3C2529::AID-SIM274%3E3.0.CO;2-5>,
  Friedman (2001) <doi:10.1214/aos/1013203451>, Apley and Zhu (2020)
  <doi:10.1111/rssb.12377>, and Lundberg and Lee (2017)
  <https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions>,
  and other related methods for survival modeling, prediction, and
  interpretation.