Simulate age-structured populations that vary in space and time and 
    explore the efficacy of a range of built-in or user-defined sampling 
    protocols to reproduce the population parameters of the known population. 
    (See Regular et al. (2020) <doi:10.1371/journal.pone.0232822> for more
    details).

Streamlines the creation of descriptive frequency tables ('Table 1'), 
    diagnostic test accuracy evaluations (sensitivity, specificity, predictive values), 
    and multi-outcome regression summaries. Features automatic tables, 
    prevalence and odds ratio calculations, and seamless integration with 'flextable' 
    for exporting results to 'Microsoft Word' and 'PowerPoint'.

Transforms or simulates data with a target empirical covariance matrix supplied
             by the user. The method to obtain the data with the target empirical covariance
             matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019)
             <arXiv:1812.05678>.

Tool for statistical simulations that have two components. 
    One component generates the data and the other one
    analyzes the data. The main aims of the package are the reduction
    of the administrative source code (mainly loops and management code for the
    results) and a simple applicability of the package that allows the user to
    quickly learn how to work with it. Parallel computing is
    also supported. Finally, convenient functions are provided to summarize the
    simulation results.

Sample size estimation for bio-equivalence trials is supported through a simulation-based approach 
    that extends the Two One-Sided Tests (TOST) procedure. The methodology provides flexibility in 
    hypothesis testing, accommodates multiple treatment comparisons, and accounts for correlated endpoints. 
    Users can model complex trial scenarios, including parallel and crossover designs, intra-subject variability, 
    and different equivalence margins. Monte Carlo simulations enable accurate estimation of power and type I error 
    rates, ensuring well-calibrated study designs. The statistical framework builds on established methods for 
    equivalence testing and multiple hypothesis testing in bio-equivalence studies, as described in Schuirmann (1987) 
    <doi:10.1007/BF01068419>, Mielke et al. (2018) <doi:10.1080/19466315.2017.1371071>, Shieh (2022) 
    <doi:10.1371/journal.pone.0269128>, and Sozu et al. (2015) <doi:10.1007/978-3-319-22005-5>. 
    Comprehensive documentation and vignettes guide users through implementation and interpretation of results.