Allows printing of character strings as messages/warnings/etc.
    with ASCII animals, including cats, cows, frogs, chickens, ghosts,
    and more.

Remedying proportional hazards
    assumption violations of a Cox proportional hazards model using
    stepwise changepoint and time-varying coefficient methods based on 
    Cox (1972) <doi:10.1111/j.2517-6161.1972.tb00899.x> and Klein 
    and Moeschberger (1997) <doi:10.1007/978-1-4757-2728-9>.

Doubly robust estimation and inference of log hazard ratio under the Cox marginal structural model with informative censoring. An augmented inverse probability weighted estimator that involves 3 working models, one for conditional failure time T, one for conditional censoring time C and one for propensity score. Both models for T and C can depend on both a binary treatment A and additional baseline covariates Z, while the propensity score model only depends on Z. With the help of cross-fitting techniques, achieves the rate-doubly robust property that allows the use of most machine learning or non-parametric methods for all 3 working models, which are not permitted in classic inverse probability weighting or doubly robust estimators. When the proportional hazard assumption is violated, CoxAIPW estimates a causal estimated that is a weighted average of the time-varying log hazard ratio. Reference: Luo, J. (2023). Statistical Robustness - Distributed Linear Regression, Informative Censoring, Causal Inference, and Non-Proportional Hazards [Unpublished doctoral dissertation]. University of California San Diego.; Luo & Xu (2022) <doi:10.48550/arXiv.2206.02296>; Rava (2021) <https://escholarship.org/uc/item/8h1846gs>.

The implementation of bias-corrected sandwich variance estimators for the analysis of cluster randomized trials with time-to-event outcomes using the marginal Cox model, proposed by Wang et al. (under review).

Provides routines for fitting Cox models by likelihood based
    boosting for single event survival data with right censoring or in the
    presence of competing risks. The methodology is described in Binder
    and Schumacher (2008) <doi:10.1186/1471-2105-9-14> and Binder et al.
    (2009) <doi:10.1093/bioinformatics/btp088>.