Functions to support rigorous processes in data cleaning, 
     evaluation, and documentation across datasets from different studies based 
     on Maelstrom Research guidelines. The package includes the core functions 
     to evaluate and format the main inputs that define the process, diagnose 
     errors, and summarize and evaluate datasets and their associated 
     data dictionaries. The main outputs are clean datasets and associated 
     metadata, and tabular and visual summary reports. As described in 
     Maelstrom Research guidelines for rigorous retrospective data 
     harmonization (Fortier I and al. (2017) <doi:10.1093/ije/dyw075>).

This function allows to generate two biological conditions synthetic 
             microarray dataset which has similar behavior to those currently 
             observed with common platforms. User provides a subset of parameters.
             Available default parameters settings can be modified.

A bundle of functions for modifying MAESTRA/MAESPA input files,reading output files, and visualizing the stand in 3D. Handy for running sensitivity analyses, scenario analyses, etc.

Data class for increased interoperability working with
    spatial-temporal data together with corresponding functions and
    methods (conversions, basic calculations and basic data manipulation).
    The class distinguishes between spatial, temporal and other dimensions
    to facilitate the development and interoperability of tools build for
    it. Additional features are name-based addressing of data and internal
    consistency checks (e.g. checking for the right data order in
    calculations).

Use a 'glmmkin' class object (GMMAT package) from the null model to perform generalized linear mixed model-based single-variant and variant set main effect tests, gene-environment interaction tests, and joint tests for association, as proposed in Wang et al. (2020) <DOI:10.1002/gepi.22351>.

Provides fast and accurate inference for the parameter estimation problem in Ordinary Differential
    Equations, including the case when there are unobserved system components. Implements the MAGI method
    (MAnifold-constrained Gaussian process Inference) of Yang, Wong, and Kou (2021) <doi:10.1073/pnas.2020397118>.
    A user guide is provided by the accompanying software paper Wong, Yang, and Kou (2024) <doi:10.18637/jss.v109.i04>.