Provides a toolbox that allows the user to implement accessibility related concepts.

The SAVVY (Survival Analysis for AdVerse Events with VarYing Follow-Up Times)
    project is a consortium of academic and pharmaceutical
    industry partners that aims to improve the analyses of adverse event (AE)
    data in clinical trials through the use of survival techniques appropriately
    dealing with varying follow-up times and competing events, see
    Stegherr, Schmoor, Beyersmann, et al. (2021) <doi:10.1186/s13063-021-05354-x>.
    Although statistical methodologies have advanced,
    in AE analyses often the incidence proportion, the incidence density or a
    non-parametric Kaplan-Meier estimator are used, which either ignore censoring or
    competing events. This package contains functions to easily conduct the
    proposed improved AE analyses.

The SAWNUTI algorithm performs sequence comparison for finite sequences of discrete events with non-uniform time intervals. Further description of the algorithm can be found in the paper: A. Murph, A. Flynt, B. R. King (2021). Comparing finite sequences of discrete events with non-uniform time intervals, Sequential Analysis, 40(3), 291-313. <doi:10.1080/07474946.2021.1940491>.

Tests coefficients with sandwich estimator of variance and with small samples. Regression types supported are gee, linear regression, and conditional logistic regression.

Spectral and Average Autocorrelation Zero Distance Density
    ('sazed') is a method for estimating the season length of a 
    seasonal time series. 'sazed' is aimed at practitioners, as it employs only 
    domain-agnostic preprocessing and does not depend on parameter tuning or 
    empirical constants. The computation of 'sazed' relies on the efficient 
    autocorrelation computation methods suggested by Thibauld Nion (2012, URL: 
    <https://etudes.tibonihoo.net/literate_musing/autocorrelations.html>) and by 
    Bob Carpenter (2012, URL: 
    <https://lingpipe-blog.com/2012/06/08/autocorrelation-fft-kiss-eigen/>).