LumPy: The LumPy toolset implements lumping for Pair-Approximation (PA) and Degree-Based Mean Field (DBMF) equations for contact processes on complex networks. It reduces the large number of ODEs given by the PA or DBMF equations by clustering them and solving instead just a single ODE per cluster. LumPy is written in Python 3 (requiring SciPy) and published under GPL v3 license.
H(O)TA Tool: H(O)TA is a tool capable of identifying the methylation and hydroxylation levels, as well as the efficiencies of the involved enzymes, in a methylation process up to single CpG level. As input it uses time course Bisulfite (and) oxBisulfite data and it comes together with a convenient user interface.
SHAVE Tool: SHAVE is a tool for the stochastic hybrid analysis of Markov population models, that is, Markov processes with an underlying population structure. It efficiently computes an accurate approximation of the probability distribution at a particular time instant based on a stochastic hybrid model that combines moment-based and state-based representations of probability distributions.
Geobound Tool: Given a transition class description of a continuous-time Markov chain model, Geobound uses geometric bounding techniques to determine a window in the state space that contains most of the long-run probability mass.