Tests whether multivariate ordinal data may stem from discretizing a multivariate normal distribution.
             The test is described by Foldnes and Grønneberg (2019) <doi:10.1080/10705511.2019.1673168>. 
             In addition, an adjusted polychoric correlation estimator is provided that takes marginal knowledge into account,
             as described by Grønneberg and Foldnes (2022) <doi:10.1037/met0000495>.

Functions for discordant kinship modeling (and other sibling-based quasi-experimental designs). Contains data restructuring functions and functions for generating biometrically informed data for kin pairs. See [Garrison and Rodgers, 2016 <doi:10.1016/j.intell.2016.08.008>], [Sims, Trattner, and Garrison, 2024 <doi:10.3389/fpsyg.2024.1430978>] for empirical examples, and Garrison and colleagues for theoretical work <https://osf.io/zpdwt/>.

Analyzes group patterns using discourse analysis data with
    graph theory mathematics. Takes the order of which individuals talk
    and converts it to a network edge and weight list. Returns the
    density, centrality, centralization, and subgroup information for each
    group. Based on the analytical framework laid out in Chai et al.
    (2019) <doi:10.1187/cbe.18-11-0222>.

Performs an exploratory data analysis through a 'shiny' interface. It includes basic methods such as the mean, median, mode, normality test, among others. It also includes clustering techniques such as Principal Components Analysis, Hierarchical Clustering and the K-Means Method.

Interactive 'R' tutorials and datasets for the textbook Field
    (2026), "Discovering Statistics Using R and RStudio",
    <https://www.discovr.rocks/>.  Interactive tutorials cover general
    workflow in 'R' and 'RStudio', summarizing data, visualizing data,
    fitting models and bias, correlation, the general linear model (GLM),
    moderation, mediation, missing values, comparing means using the GLM
    (analysis of variance), comparing adjusted means (analysis of covariance), factorial
    designs, repeated measures designs, exploratory factor analysis (EFA).
    There are no functions, only datasets and interactive tutorials.