Functions to 
        calculate power and sample size for testing
        (1) mediation effects; 
        (2) the slope in a simple linear regression; 
        (3) odds ratio in a simple logistic regression;
        (4) mean change for longitudinal study with 2 time points;
        (5) interaction effect in 2-way ANOVA; and
        (6) the slope in a simple Poisson regression.

Model-implied simulation-based power estimation (MSPE) for
    nonlinear (and linear) SEM, path analysis and regression analysis. A
    theoretical framework is used to approximate the relation between
    power and sample size for given type I error rates and effect sizes.
    The package offers an adaptive search algorithm to find the optimal N for
    given effect sizes and type I error rates. Plots can be used to visualize
    the power relation to N for different parameters of interest (POI). 
    Theoretical justifications are given in Irmer et al. 
    (2024a) <doi:10.31219/osf.io/pe5bj> and detailed description
    are given in Irmer et al. (2024b) <doi:10.3758/s13428-024-02476-3>.

There are two main functions: (1) To estimate the power of testing for linkage using an
        affected sib pair design, as a function of the recurrence risk
        ratios. We will use analytical power formulae as implemented in
        R. These are based on a Mathematica notebook created by Martin
        Farrall. (2) To examine how the power of the transmission
        disequilibrium test (TDT) depends on the disease allele
        frequency, the marker allele frequency, the strength of the
        linkage disequilibrium, and the magnitude of the genetic
        effect. We will use an R program that implements the power
        formulae of Abel and Muller-Myhsok (1998). These formulae allow
        one to quickly compute power of the TDT approach under a
        variety of different conditions. This R program was modeled on
        Martin Farrall's Mathematica notebook.

It estimates power and sample size for Partial Least Squares-based methods described in Andreella, et al., (2024), <doi:10.48550/arXiv.2403.10289>.