Computing the one-sided/two-sided integrated/maximally selected EL statistics for simultaneous testing, the one-sided/two-sided EL tests for pointwise testing, and an initial test that precedes one-sided testing to exclude the possibility of crossings or alternative orderings among the survival functions.

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.

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>.

Performs canonical correlation for survey data, including 
  multiple tests of significance for secondary canonical correlations. 
  A key feature of this package is that it incorporates survey data structure 
  directly in a novel test of significance via a sequence of simple linear 
  regression models on the canonical variates. See reference - Cruz-Cano, 
  Cohen, and Mead-Morse (2024) "Canonical Correlation Analysis of Survey data: the SurveyCC R package" 
  The R Journal under review.