JumpProcesses.jl, formerly DiffEqJump.jl, provides methods for simulating jump processes, known as stochastic simulation algorithms (SSAs), Doob's method, Gillespie methods, or Kinetic Monte Carlo methods across different fields of science. It also enables the incorporation of jump processes into hybrid jump-ODE and jump-SDE models, including jump diffusions.
The documentation includes
- a tutorial on simulating basic Poisson processes
- a tutorial and details on using JumpProcesses to simulate jump processes via SSAs (i.e. Gillespie methods),
- a tutorial on simulating jump-diffusion processes,
- a reference on the types of jumps and available simulation methods,
- a reference on jump time stepping methods
- a FAQ with information on changing parameters between simulations and using callbacks.
- the JumpProcesses.jl API documentation.
There are two ways to install
JumpProcesses.jl. First, users may install the meta
DifferentialEquations.jl package, which installs and wraps
OrdinaryDiffEq.jl for solving ODEs,
StochasticDiffEq.jl for solving SDEs, and
JumpProcesses.jl, along with a number of other useful packages for solving models involving ODEs, SDEs and/or jump process. This single install will provide the user with all of the facilities for developing and solving Jump problems.
To install the
DifferentialEquations.jl package, refer to the following link for complete installation details.
If the user wishes to separately install the
JumpProcesses.jl library, which is a lighter dependency than
DifferentialEquations.jl, then the following code will install
JumpProcesses.jl using the Julia package manager:
using Pkg Pkg.add("JumpProcesses")
- Please refer to the SciML ColPrac: Contributor's Guide on Collaborative Practices for Community Packages for guidance on PRs, issues, and other matters relating to contributing to SciML.
- There are a few community forums:
See also the SciML Community page.