Provides a function for estimating the transition probabilities in an illness-death model. 
            The transition probabilities can be estimated from the unsmoothed landmark estimators developed 
            by de Una-Alvarez and Meira-Machado (2015) <doi:10.1111/biom.12288>.
            Presmoothed estimates can also be obtained through the use of a parametric family of binary
            regression curves, such as logit, probit or cauchit. The additive logistic regression model 
            and nonparametric regression are also alternatives which have been implemented.
            The idea behind the presmoothed landmark estimators is to use the presmoothing techniques 
            developed by  Cao et al. (2005) <doi:10.1007/s00180-007-0076-6> in the 
            landmark estimation of the transition probabilities.

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>.