A method to explore the treatment-covariate interactions in survival or generalized 
	linear model (GLM) for continuous, binomial and count data arising from two or more treatment 
	arms of a clinical trial. A permutation distribution approach to inference is implemented, 
	based on permuting the covariate values within each treatment group.

Tools for power and sample size 
    calculation as well as design diagnostics for 
    longitudinal mixed model settings, with a focus on stepped wedge designs.
    All calculations are oracle estimates i.e. assume random effect variances 
    to be known (or guessed) in advance.  
    The method is introduced in Hussey and Hughes (2007) <doi:10.1016/j.cct.2006.05.007>,
    extensions are discussed in Li et al. (2020) <doi:10.1177/0962280220932962>.

Provide various functions and tools to help fit models for
    estimating treatment effects in stepped wedge cluster randomized trials.
    Implements methods described in Kenny, Voldal, Xia, and Heagerty (2022) 
    "Analysis of stepped wedge cluster randomized trials in the presence of a 
    time-varying treatment effect", <doi:10.1002/sim.9511>.

Model Selection Based on Combined Penalties. This package implements a stepwise forward variable selection algorithm based on a penalized likelihood criterion that combines the L0 with L2 or L1 norms.

L2 penalized logistic regression for both continuous and discrete predictors, with forward stagewise/forward stepwise variable selection procedure.

Allows fitting of step-functions to univariate serial data where neither the number of jumps nor their positions is known by implementing the multiscale regression estimators SMUCE, simulataneous multiscale changepoint estimator, (K. Frick, A. Munk and H. Sieling, 2014) <doi:10.1111/rssb.12047> and HSMUCE, heterogeneous SMUCE, (F. Pein, H. Sieling and A. Munk, 2017) <doi:10.1111/rssb.12202>. In addition, confidence intervals for the change-point locations and bands for the unknown signal can be obtained.