This is an interface for the 'Python' package
  'StepMix'. It is a 'Python' package following the scikit-learn API for
  model-based clustering and generalized mixture modeling (latent class/profile
  analysis) of continuous and categorical data. 'StepMix' handles missing values
  through Full Information Maximum Likelihood (FIML) and provides multiple stepwise
  Expectation-Maximization (EM) estimation methods based on pseudolikelihood
  theory. Additional features include support for covariates and distal outcomes,
  various simulation utilities, and non-parametric bootstrapping, which allows
  inference in semi-supervised and unsupervised settings. Software paper available
  at <doi:10.18637/jss.v113.i08>.

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.