This is a computational package designed to identify the most sensitive interactions within a network which must be estimated most accurately in order to produce qualitatively robust predictions to a press perturbation. This is accomplished by enumerating the number of sign switches (and their magnitude) in the net effects matrix when an edge experiences uncertainty. The package produces data and visualizations when uncertainty is associated to one or more edges in the network and according to a variety of distributions. The software requires the network to be described by a system of differential equations but only requires as input a numerical Jacobian matrix evaluated at an equilibrium point. This package is based on Koslicki, D., & Novak, M. (2017) <doi:10.1007/s00285-017-1163-0>.

Allows biomechanical pressure data from a range of systems to be imported and processed in a reproducible manner. Automatic and manual tools are included to let the user define regions (masks) to be analyzed. Also includes functions for visualizing and animating pressure data. Example methods are described in Shi et al., (2022) <doi:10.1038/s41598-022-19814-0>, Lee et al., (2014) <doi:10.1186/1757-1146-7-18>, van der Zward et al., (2014) <doi:10.1186/1757-1146-7-20>, Najafi et al., (2010) <doi:10.1016/j.gaitpost.2009.09.003>, Cavanagh and Rodgers (1987) <doi:10.1016/0021-9290(87)90255-7>.

This repository contains the codes for using the predictive accuracy comparison tests developed in Pitarakis, J. (2023) <doi:10.1017/S0266466623000154>.

An application to calculate a patient's pretest probability
    (PTP) for obstructive Coronary Artery Disease (CAD) from a collection
    of guidelines or studies. Guidelines usually comes from the American
    Heart Association (AHA), American College of Cardiology (ACC) or
    European Society of Cardiology (ESC). Examples of PTP scores that
    comes from studies are the 2020 Winther et al. basic, Risk
    Factor-weighted Clinical Likelihood (RF-CL) and Coronary Artery
    Calcium Score-weighted Clinical Likelihood (CACS-CL) models
    <doi:10.1016/j.jacc.2020.09.585>, 2019 Reeh et al. basic and clinical
    models <doi:10.1093/eurheartj/ehy806> and 2017 Fordyce et al. PROMISE
    Minimal-Risk Tool <doi:10.1001/jamacardio.2016.5501>.  As diagnosis of
    CAD involves a costly and invasive coronary angiography procedure for
    patients, having a reliable PTP for CAD helps doctors to make better
    decisions during patient management.  This ensures high risk patients
    can be diagnosed and treated early for CAD while avoiding unnecessary
    testing for low risk patients.