A Package for selecting variables for the joint modeling of mean and dispersion (including models for mixture experiments) based on hypothesis testing and the quality of model's fit. In each iteration of the selection process, a criterion for checking the goodness of fit is used as a filter for choosing the terms that will be evaluated by a hypothesis test. Pinto & Pereira (2021) <arXiv:2109.07978>.

Provides functions to calculate step- and cadence-based metrics from 
    timestamped accelerometer and wearable device data. Supports CSV and AGD files from 
    'ActiGraph' devices, CSV files from 'Fitbit' devices, and step counts derived   
    with R package 'GGIR' <https://github.com/wadpac/GGIR>, with automatic handling 
    of epoch lengths from 1 to 60 seconds. Metrics include total steps, cadence 
    peaks, minutes and steps in predefined cadence bands, and time and steps in 
    moderate-to-vigorous physical activity (MVPA). Methods and thresholds are 
    informed by the literature, e.g., 
    Tudor-Locke and Rowe (2012) <doi:10.2165/11599170-000000000-00000>, 
    Barreira et al. (2012) <doi:10.1249/MSS.0b013e318254f2a3>, 
    and Tudor-Locke et al. (2018) <doi:10.1136/bjsports-2017-097628>. 
    The package record is also available on Zenodo (2023) <doi:10.5281/zenodo.7858094>.

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>.