Provides fast alternatives to standard survival analysis functions
    in the 'survival' package, together with tools for time-to-event trial
    simulation and sequential analysis. The estimation and testing functions
    cover a single-time-point Kaplan-Meier estimator (survfit_fast()), log-rank
    tests including weighted and stratified variants (survdiff_fast()), a
    closed-form hazard ratio estimator based on the Pike-Halley Estimator method
    (coxph_fast()), restricted mean survival time (rmst_fast()), window mean
    survival time (wmst_fast()), milestone survival comparison
    (milestone_fast()), median survival time (medsurv_fast()), the max-combo
    test (maxcombo_fast()), the robust modestly-weighted log-rank test
    (rmw_fast()), the weighted Kaplan-Meier (Pepe-Fleming) test (wkm_fast()),
    the average hazard with survival weight (ahsw_fast()), and the
    Kalbfleisch-Prentice average hazard ratio (ahr_fast()). The simulation
    layer generates individual patient data (simdata_fast()), performs interim
    or sequential analyses (analysis_fast()), and aggregates operating
    characteristics (simsummary_fast()). A visualization layer assembles
    design-stage scenarios (gen_scenario_fast()) and builds analysis-stage
    Kaplan-Meier curves (kmcurve_fast()), each with plot and print methods.
    All functions are designed for repeated
    evaluation inside large simulation loops, such as adaptive sample-size
    re-estimation, probability-of-success calculations, and regional consistency
    evaluation in multi-regional trials. Core computations are implemented in
    'C++' via 'Rcpp' for maximum performance. Methodological background is
    described in Collett (2014, ISBN:9780429196294).

An interface to the 'fastText' <https://github.com/facebookresearch/fastText> library for efficient learning of word representations and sentence classification. The 'fastText' algorithm is explained in detail in (i) "Enriching Word Vectors with subword Information", Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomas Mikolov, 2017, <doi:10.1162/tacl_a_00051>; (ii) "Bag of Tricks for Efficient Text Classification", Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov, 2017, <doi:10.18653/v1/e17-2068>; (iii) "FastText.zip: Compressing text classification models", Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Herve Jegou, Tomas Mikolov, 2016, <doi:10.48550/arXiv.1612.03651>.