Calculate various indices, like Crude Migration Rate,
    different Gini indices or the Coefficient of Variation among others, to
    show the (un)equality of migration.

Detection of migration events and segments of continuous residence 
    based on irregular time series of location data
    as published in Chi et al. (2020) <doi:10.1371/journal.pone.0239408>.

This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.

Contains functions for data analysis of Repeated measurement using GEE. Data may contain missing value in response and covariates. For parameter estimation through Fisher Scoring algorithm, Mean Score and Inverse Probability Weighted method combining with Multiple Imputation are used when there is missing value in covariates/response.
             Reference for mean score method, inverse probability weighted method is
             Wang et al(2007)<doi:10.1093/biostatistics/kxl024>.

Data-driven approach for Exploratory Factor Analysis (EFA)
    that uses Model Implied Instrumental Variables (MIIVs). The method
    starts with a one factor model and arrives at a suggested model with
    enhanced interpretability that allows cross-loadings and correlated
    errors.

Functions for estimating structural equation models using 
    instrumental variables.

An interface to build machine learning models for
    classification and regression problems. 'mikropml' implements the ML
    pipeline described by Topçuoğlu et al. (2020)
    <doi:10.1128/mBio.00434-20> with reasonable default options for data
    preprocessing, hyperparameter tuning, cross-validation, testing, model
    evaluation, and interpretation steps.  See the website
    <https://www.schlosslab.org/mikropml/> for more information,
    documentation, and examples.

Microbial growth is often measured by growth curves i.e. a table of population sizes and times of measurements. 
  This package allows to use such growth curve data to determine the duration of "microbial lag phase" i.e. the time needed for microbes to restart divisions.
  It implements the most commonly used methods to calculate the lag duration, these methods are discussed and described in Opalek et.al. 2022.
  Citation: "How to determine microbial lag phase duration?", M. Opalek, B. Smug, D. Wloch-Salamon (2022) <doi:10.1101/2022.11.16.516631>.