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>.

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 (2023), Biostatistics (To appear).

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.

Electrical properties of resistor networks using matrix methods.

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>.

The Bayesian modelling of relative sea-level data
    using a comprehensive approach that incorporates
    various statistical models within a unifying framework. 
    Details regarding each statistical models; 
    linear regression (Ashe et al 2019) <doi:10.1016/j.quascirev.2018.10.032>, 
    change point models (Cahill et al 2015) <doi:10.1088/1748-9326/10/8/084002>, 
    integrated Gaussian process models (Cahill et al 2015) <doi:10.1214/15-AOAS824>, 
    temporal splines (Upton et al 2023) <arXiv:2301.09556>, 
    spatio-temporal splines (Upton et al 2023) <arXiv:2301.09556> and
    generalised additive models (Upton et al 2023) <arXiv:2301.09556>.
    This package facilitates data loading, 
    model fitting and result summarisation.
    Notably, it accommodates the inherent measurement errors
    found in relative sea-level data across multiple dimensions,
    allowing for their inclusion in the statistical models.