Survival analysis models are commonly used in medicine and other areas. Many of them
    are too complex to be interpreted by human. Exploration and explanation is needed, but
    standard methods do not give a broad enough picture. 'survex' provides easy-to-apply
    methods for explaining survival models, both complex black-boxes and simpler statistical models.
    They include methods specific to survival analysis such as SurvSHAP(t) introduced in Krzyzinski et al., (2023)
    <doi:10.1016/j.knosys.2022.110234>, SurvLIME described in Kovalev et al., (2020) <doi:10.1016/j.knosys.2020.106164> as well as
    extensions of existing ones described in Biecek et al., (2021) <doi:10.1201/9780429027192>.

It computes Relative survival, AER and SMR based on French death rates.

Survival analysis using a flexible Bayesian model for individual-level right-censored data, optionally combined with aggregate data on counts of survivors in different periods of time. An M-spline is used to describe the hazard function, with a prior on the coefficients that controls over-fitting. Proportional hazards or flexible non-proportional hazards models can be used to relate survival to predictors. Additive hazards (relative survival) models, waning treatment effects, and mixture cure models are also supported. Priors can be customised and calibrated to substantive beliefs. Posterior distributions are estimated using 'Stan', and outputs are arranged in a tidy format. See Jackson (2023) <doi:10.1186/s12874-023-02094-1>.

Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase subsampling designs. Graphics. PPS sampling without replacement. Small-area estimation.

Implements different kinds of bootstraps
    to estimate sampling variation from survey data with 
    complex designs. Includes the rescaled bootstrap
    described in Rust and Rao (1996) <doi:10.1177/096228029600500305> and
    Rao and Wu (1988) <doi:10.1080/01621459.1988.10478591>.