MCHammer.jl is a package designed in raw Julia to quickly analyze probabilistic and build Monte-Carlo simulations more easily and with less lines of code. A complete set of charts and functions to analyze, visualize, correlate, import variables and export results are included. Check out the package and join in the fun as a contributor.
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You want a link there…
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Thanks for posting the link :slight_smile
I guess I missed adding the link 
This looks useful for engineering problems. I’ll have to give this a try soon.
I see some issues regarding the docs.
- Stable and Dev both point to Dev (maybe that’s just the case right now).
- The link under Tutorials ( Getting Started with MCHammer ) fails for me as does the Edit on GitHub link at the top of the page (404s).
Does your package comes with a theme song?
Theme song for MCHammer Package
I wanna use your package but unfortunately I “can’t touch this”
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Still new to publishing. Ill address the doc issues. Any help would be appreciated if you have time.
best,
Eric
I was looking through the documentation and readme but couldn’t really understand what problems you solve. It would be beneficial for your project to, in the readme or intro in docs, state who this package is for, what problems you solve and how your package relates to other MC tools in the julia ecosystems, such as Turing.jl etc. You also provide som visualization tools, how do those relate to, e.g., StatsPlots.jl, ArviZ.jl, VegaLite.jl etc.?
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Ok, looks bad but I just saw your message. I am revamping the documentation and I will see if I can make it more clear. I tend to assume that most people have worked with Excel based monte-carlo simulation which this package seeks to address.
To your point, the problem I am solving is making it possible to interact with your analysis and save many, many lines of code. For example, you can drawe histograms and sensitivity charts with simple functions. Furthermore, there was no implementation of Rank Order correlation…which is very important when working with data from multiple sources.
I also forgot to mention that the tutorials section could give you a sense. Because you can model anything with MCS, I have elected to use finance and economic models as the basis to explain the functionality… but you could be modelling the growth rates of mosquitoes for all i know
Right now we have decided to host the documentation while we implement the new Documenter workflow.