Computes a single scalar metric for diurnal cortisol cycle analysis,
    the Cortisol Sine Score (CSS). The score is calculated as the sum over time
    points of concentration multiplied by sin(2 * pi * time / 24), giving positive
    weights to morning time points and negative weights to evening ones. The method
    is model-free, robust, and suitable for regression, classification, clustering,
    and biomarker research.

Create correlation (or partial correlation) matrices. Correlation matrices are formatted with significance stars based on user preferences. Matrices of coefficients, p-values, and number of pairwise observations are returned. Send resultant formatted matrices to the clipboard to be pasted into excel and other programs. A plot method allows users to visualize correlation matrices created with 'corx'.

Implements bound constrained optimal sample size allocation (BCOSSA) framework described in Bulus & Dong (2021) <doi:10.1080/00220973.2019.1636197> for power analysis of multilevel regression discontinuity designs (MRDDs) and multilevel randomized trials (MRTs) with continuous outcomes.
  Minimum detectable effect size (MDES) and power computations for MRDDs allow polynomial functional form specification for the score variable (with or without interaction with the treatment indicator). See Bulus (2021) <doi:10.1080/19345747.2021.1947425>.

Computes the cosine-correlation coefficient for measuring 
    the degree of linear dependence among variables in a multidimensional context. 
    The package implements the generalized cosine-correlation theorem for p-1 variables, 
    providing a quantitative assessment of interrelationships within experimental frameworks. 
    This methodology extends classical correlation measures to higher-dimensional spaces 
    using a dimensional exploration approach based on time scale calculus.

Fast fitting of Stable Isotope Mixing Models in R. Allows for the inclusion of covariates. Also has built-in summary functions and plot functions which allow for the creation of isospace plots. Variational Bayes is used to fit these models, methods as described in: Tran et al., (2021) <doi:10.48550/arXiv.2103.01327>.