Cuddy-Della valle index gives the degree of instability present in the data by accommodating the effect of a trend. The adjusted R squared value of the best fitted model is chosen. The index is obtained by multiplying the coefficient of variation with square root of one minus the adjusted R-squared value. This package has been developed using concept of Shankar et al. (2022)<doi:10.3389/fsufs.2023.1208898>.

Provides tools for trial-based economic evaluation of healthcare interventions. 
    Computes and visualizes incremental cost-effectiveness ratios, 
    cost-effectiveness acceptability curves, cost-effectiveness planes, 
    net monetary benefit tables, and one-way deterministic sensitivity analyses. 
    Supports cost-utility analyses using observed summaries and non-parametric 
    bootstrap uncertainty.

Measuring cellular energetics is essential to understanding a
    matrix’s (e.g. cell, tissue or biofluid) metabolic state. The Agilent
    Seahorse machine is a common method to measure real-time cellular
    energetics, but existing analysis tools are highly manual or lack
    functionality. The Cellular Energetics Analysis Software (ceas) R package
    fills this analytical gap by providing modular and automated Seahorse data
    analysis and visualization using the methods described by Mookerjee et al.
    (2017) <doi:10.1074/jbc.m116.774471>.

Gather boxscore and play-by-play data from the Canadian Elite Basketball League (CEBL) <https://www.cebl.ca> to create a repository of basic and advanced statistics for teams and players.

Splits data into Gaussian type clusters using the Cross-Entropy 
    Clustering ('CEC') method. This method allows for the simultaneous use of 
    various types of Gaussian mixture models, for performing the reduction of 
    unnecessary clusters, and for discovering new clusters by splitting them. 
    'CEC' is based on the work of Spurek, P. and Tabor, J. (2014) 
    <doi:10.1016/j.patcog.2014.03.006>.

Provides analytical methods for analyzing CRISPR screen data
    at different levels of gene expression. Multi-component normal mixture models
    and EM algorithms are used for modeling.