MCHammer.jl
Overview
The MC in MC Hammer stands for Monte-Carlo. This tool is inspired by seminal tools such as Oracle Crystal Ball and Palisade @RISK for their ability to quickly build and analyze Monte-Carlo simulation models using Excel functions and automations. MC Hammer replicates their logic, functions and elemental tools in Julia, thus significantly reducing the time, lines of code, complexity and effort to perform advanced modeling and simulation.
Installing MCHammer
Install the package as usual using Pkg.
using Pkg
Pkg.("MCHammer")
If you need to install direct, we recommend using ']' to go in the native Pkg manager.
(v1.1) pkg> add https://github.com/etorkia/MCHammer.jl
Loading MCHammer
To load the MCHammer package
using MCHammer
Getting your environment setup for modeling
In order to build your first model, you will need to get a few more packages installed:
- Distributions.jl : To build a simulation, you need distributions as inputs. Julia offers univariate and multivariate distributions covering most needs.
- StatsBase.jl and Statistics.jl : These packages provide all the functions to analyze results and build models.
To load the support packages:
julia> using Distributions, Statistics, StatsBase, DataFrames
Tutorials
Index
MCHammer.GBMA_d
MCHammer.GBMM
MCHammer.GBMMfit
MCHammer.GetCertainty
MCHammer.RiskEvent
MCHammer.cmd
MCHammer.cormat
MCHammer.corvar
MCHammer.covmat
MCHammer.density_chrt
MCHammer.fractiles
MCHammer.genmeta
MCHammer.histogram_chrt
MCHammer.importsip
MCHammer.importxlsip
MCHammer.markov_a
MCHammer.markov_ts
MCHammer.marty
MCHammer.sensitivity_chrt
MCHammer.sip2csv
MCHammer.trend_chrt
MCHammer.truncate_digit