Julia provides incredible flexibility for migrating tedious data processing and decision/risk modeling that happens currently in Excel but no packages existed to automate or simplify the processing of simulation results. Inspired by the many Excel add-ins that were created to automate the many steps in the Monte-Carlo Simulation algorithm (including correlation and results visualization), we created a similar time-saving toolkit for Julia called MCHammer.
MCHammer provides all the tools of modern simulation tools add-ins (such as Oracle Crystal Ball, Palisade @Risk, etc. ) in a simplified command line. Major features to cust modeling time and time to answer are discussed :
• Correlation of Simulated Inputs (Iman-Conover[1982])
o Covariance Matrix
o Rank Order Correlation Matrix
o Pearson Product Moment Correlation Matrix
• Data Analytics tools for analyzing both simulation results and exploratory analysis of historical datasets
o Density & Histogram Charts with Descriptive Stats
o Density
o Histogram
o TimeSeries Trend Chart
o Sensitivity Analysis Charts
• Sensitivity Analysis Chart
o Rank Correlation
o PPMC
o Contribution to Variance %
• TimeSeries simulation
o Time Series using Simulated Random Walk
o Time Series using Historical Data to calculate parameters for Simulated Random Walk
o Trend Charts with Customizable Confidence Bands
• Import / Export of results using the SIPMath standard.