Please refer to each file (.jl and .R) to find the required packages prior to running the codes.
The file, cohmod.jl, is required for running the examples in Julia. One key package is DifferentialEquations package (https://github.com/JuliaDiffEq/DifferentialEquations.jl).
The R codes are self-contained. One key package is the YUIMA package (https://github.com/cran/yuima).
Julia language is not used widespread in decision modeling. Here are some of the main sites for new users.
Julia is a high-level, high-performance dynamic language for technical computing. The main homepage for Julia can be found at julialang.org. This is the GitHub repository of Julia source code, including instructions for compiling and installing Julia, below.
The Julia REPL is quite powerful. See the section in the manual on the Julia REPL for more details.
Support for editing Julia is available for many widely used editors: Emacs, Vim, Sublime Text, and many others.
Supported IDEs include: Juno (Atom plugin), julia-vscode (VS Code plugin), and julia-intellij (IntelliJ IDEA plugin). The popular Jupyter notebook interface is available through IJulia.
- Homepage: https://julialang.org
- Binaries: https://julialang.org/downloads/
- Source code: https://github.com/JuliaLang/julia
- Documentation: https://docs.julialang.org/
- Packages: https://pkg.julialang.org/
- Discussion forum: https://discourse.julialang.org
- Slack: https://julialang.slack.com (get an invite from https://slackinvite.julialang.org)
- YouTube: https://www.youtube.com/user/JuliaLanguage
The R and Julia codes are used in the manuscript titled "Adding noise to Markov cohort models."
Please email author at rowan.iskandar@gmail.com for any questions.
Example 1 is based on: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0205543
Example 2 is based on: https://onlinelibrary.wiley.com/doi/abs/10.1111/tbj.12757