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>.

Electrical properties of resistor networks using matrix methods.

A 'shiny' module to facilitate page layouts with resizable panes for page content based on 'split.js' 'JavaScript' library (<https://split.js.org>).

A pair of functions for calculating mean residual life (MRL)
    , median residual life, and percentile residual life using the outputs of 
    either the 'flexsurv' package or parameters
    provided by the user. Input information about the distribution, the given
    'life' value, the percentile, and the type of residual life, 
    and the function will return your desired values. For the 'flexsurv' option,
    the function allows the user to input their own data for making predictions. 
    This function is based on Jackson (2016) <doi:10.18637/jss.v070.i08>.