This is the companion package to the Data Visualization Geometries Encyclopedia, providing seamless access to the associated data.

Offers robust tools to identify and manage incomplete responses in survey datasets, thereby enhancing the quality and reliability of research findings.

Analysis and visualization of dropout between conditions in surveys and (online) experiments. Features include computation of dropout statistics, comparing dropout between conditions (e.g. Chi squared), analyzing survival (e.g. Kaplan-Meier estimation), comparing conditions with the most different rates of dropout (Kolmogorov-Smirnov) and visualizing the result of each in designated plotting functions. Article published in _Behavior Research Methods_ on 'dropR' by the authors: Dropout analysis: A method for data from Internet-based research and 'dropR', an R-based web app and package to analyze and visualize dropout. (2025) <doi:10.3758/s13428-025-02730-2>. Sources: Andrea Frick, Marie-Terese Baechtiger & Ulf-Dietrich Reips (2001) <doi:10.5167/uzh-19758>; Ulf-Dietrich Reips (2002) <doi:10.1026//1618-3169.49.4.243>.

Generates simulated data representing the LOX drop testing
    process (also known as impact testing). A simulated process allows for
    accelerated study of test behavior. Functions are provided to simulate
    trials, test series, and groups of test series. Functions for creating plots
    specific to this process are also included. Test attributes and criteria can
    be set arbitrarily. This work is not endorsed by or affiliated with NASA.
    See "ASTM G86-17, Standard Test Method for Determining Ignition Sensitivity
    of Materials to Mechanical Impact in Ambient Liquid Oxygen and Pressurized
    Liquid and Gaseous Oxygen Environments" <doi:10.1520/G0086-17>.

Efficient covariate-adjusted estimators of quantities that are useful for 
    establishing the effects of treatments on ordinal outcomes.

Provide tools for drought monitoring based on univariate and multivariate drought indicators.Statistical drought prediction based on Ensemble Streamflow Prediction (ESP), drought risk assessments, and drought propagation are also provided. Please see Hao Zengchao et al. (2017) <doi:10.1016/j.envsoft.2017.02.008>.