Performs various analyzes of descriptive statistics, including correlations, graphics and tables.

All datasets and functions required for the examples and exercises of the book "Data Science for Psychologists" (by Hansjoerg Neth, Konstanz University, 2025), freely available at <https://bookdown.org/hneth/ds4psy/>. The book and corresponding courses introduce principles and methods of data science to students of psychology and other biological or social sciences. The 'ds4psy' package primarily provides datasets, but also functions for data generation and manipulation (e.g., of text and time data) and graphics that are used in the book and its exercises. All functions included in 'ds4psy' are designed to be explicit and instructive, rather than efficient or elegant.

Seasonal- and calendar adjustment of time series
    with daily frequency using the DSA approach developed by Ollech,
    Daniel (2018): Seasonal adjustment of daily time series. Bundesbank
    Discussion Paper 41/2018.

Exploration of simulation models (apps) of various infectious disease transmission dynamics scenarios.
    The purpose of the package is to help individuals learn 
    about infectious disease epidemiology (ecology/evolution) from a dynamical systems perspective.
    All apps include explanations of the underlying models and instructions on what to do with the models.

Simulation models (apps) of various within-host immune response scenarios.
    The purpose of the package is to help individuals learn 
    about within-host infection and immune response modeling from a dynamical systems perspective.
    All apps include explanations of the underlying models and instructions on what to do with the models.

Providing six different algorithms that can be used to split the 
    available data into training, test and validation subsets with similar distribution 
    for hydrological model developments. The dataSplit() function will help you divide 
    the data according to specific requirements, and you can refer to the par.default() 
    function to set the parameters for data splitting. The getAUC() function will help 
    you measure the similarity of distribution features between the data subsets.
    For more information about the data splitting algorithms, please refer to:
    Chen et al. (2022) <doi:10.1016/j.jhydrol.2022.128340>, 
    Zheng et al. (2022) <doi:10.1029/2021WR031818>.