The aim of pySODM is to reduce the time it takes to step through this workflow. pySODM provides a template to construct, simulate and calibrate dynamical systems governed by differential equations. Models can have n-dimensional labeled states of different sizes and can be simulated deterministically and stochastically. Model parameters can be time-dependent by means of complex functions with arbitrary inputs. The labeled n-dimensional model states can be aligned with n-dimensional data to compute the posterior probability function, which can subsequently be optimised.

Julia package to facilitate the construction of JumpProblems (discrete state, continuous time stochastic processes) on graphs. The idea is to define some jumps (discrete state, continuous time, stochastic variables) for the vertices and for edges of an arbitrary Graph, and the package functionality returns a JumpSet which can be solved with JumpProcesses.jl and DifferentialEquations.jl.