Summarize model output using a robust effect size index. The index is introduced in Vandekar, Tao, & Blume (2020, <doi:10.1007/s11336-020-09698-2>). Software paper available at <doi:10.18637/jss.v112.i03>.

Developed to assist researchers with planning analysis,
    prior to obtaining data from Trusted Research Environments (TREs) also known as safe havens.
    With functionality to export and import marginal distributions as well as synthesise data, both with
    and without correlations from these marginal distributions. Using a multivariate cumulative distribution (COPULA).
    Additionally the International Stroke Trial (IST) is included as an example dataset under ODC-By licence
    Sandercock et al. (2011) <doi:10.7488/ds/104>,
    Sandercock et al. (2011) <doi:10.1186/1745-6215-12-101>.

Datasets with energy consumption data of different data measurement frequencies. 
    The data stems from several publicly funded research projects of the Chair of Information  
    Systems and Energy Efficient Systems at the University of Bamberg.

Studies of resilience in older adults employ a single-arm design where everyone experiences the stressor. The simplistic approach of regressing change versus baseline yields biased estimates due to regression-to-the-mean. This package provides a method to correct the bias. It also allows covariates to be included. The method implemented in the package is described in Varadhan, R., Zhu, J., and Bandeen-Roche, K (2024), Biostatistics 25(4): 1094-1111.

Contains various tools to perform and visualize Response Item Networks ('ResIN's'). 'ResIN' binarizes ordered-categorical and qualitative response choices from (survey) data, calculates pairwise associations and maps the location of each item response as a node in a force-directed network. Please refer to <https://www.resinmethod.net/> for more details.

An intuitive and explainable metric of Feature Importance for Classification Problems.
    Resolution Index measures the extent to which a Feature clusters different classes when data is 
    sorted on it. User provides a DataFrame, column name of the Class, sample size and number of 
    iterations used for calculation. Resolution Index for each Feature is returned, which can be 
    effectively used to rank Features and reduce Dimensionality of Training data. For more details
    on Feature Selection see Theng and Bhoyar (2023) <doi:10.1007/s10115-023-02010-5>.