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.

Implements a suite of shrinkage estimators for multivariate linear 
    regression to improve estimation stability and predictive accuracy. 
    Provides methods including the Stein estimator, Diagonal Shrinkage, 
    the general Shrinkage estimator (solving a Sylvester equation), and 
    Slab Regression (Simple and Generalized). These methods address Stein's 
    paradox by introducing structured bias to reduce variance without requiring 
    cross-validation, except for 'ShrinkageRR' where the intensity 
    is chosen by minimizing an explicit Mean Squared Error (MSE) criterion. 
    Methods are based on Asimit, V., Cidota, M. A., Chen, Z., and Asimit, J. (2025) 
    <https://openaccess.city.ac.uk/id/eprint/35005/>.

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.