Allows performing forwards prediction for the General Unified 
          Threshold model of Survival using compiled ode code. This package 
          was created to avoid dependency with the 'morse' package that requires 
          the installation of 'JAGS'. This package is based on functions from 
          the 'morse' package v3.3.1: Virgile Baudrot, Sandrine Charles, 
          Marie Laure Delignette-Muller, Wandrille Duchemin, Benoit Goussen, 
          Nils Kehrein, Guillaume Kon-Kam-King, Christelle Lopes, Philippe Ruiz, 
          Alexander Singer and Philippe Veber (2021) <https://CRAN.R-project.org/package=morse>.

Simulates a network of ordinary differential equations of order
    two. The package provides an easy interface to construct networks. In addition
    you are able to define different external triggers to manipulate the trajectory.
    The method is described by Surmann, Ligges, and Weihs (2014) <doi:10.1109/ENERGYCON.2014.6850482>.

Performs sensitivity analysis in ordinary differential equation (ode) models.
    The package utilize the ode interface from 'deSolve' and connects it with the 
    sensitivity analysis from 'sensitivity'. Additionally we add a method to
    run the sensitivity analysis on variables with class 'ODEnetwork'. A detailed
    plotting function provides outputs on the calculations.
    The method is described by Weber, Theers, Surmann, Ligges, and Weihs (2018) <doi:10.17877/DE290R-18874>.

An object is called "outlier" if it remarkably deviates from the other objects in a data set. Outlier detection is the process to find outliers by using the methods that are based on distance measures, clustering and spatial methods (Ben-Gal, 2005 <ISBN 0-387-24435-2>). It is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for outlier removing in data processing. This package provides the implementations of some novel approaches to detect the outliers based on typicality degrees that are obtained with the soft partitioning clustering algorithms such as Fuzzy C-means and its variants.