mcerp is a stochastic calculator for Monte Carlo methods that uses
latin-hypercube sampling to perform non-order specific
error propagation (or uncertainty analysis).
With this package you can easily and transparently track the effects
of uncertainty through mathematical calculations. Advanced mathematical
functions, similar to those in the standard math module, and statistical
functions like those in the scipy.stats module, can also be evaluated
If you are familiar with Excel-based risk analysis programs like @Risk,
Crystal Ball, ModelRisk, etc., this package will work wonders for you
(and probably even be faster!) and give you more modelling flexibility with
the powerful Python language. This package also doesn’t cost a penny,
compared to those commercial packages which cost thousands of dollars for a
single-seat license. Feel free to copy and redistribute this package as much
as you desire!
The following packages should be installed automatically (if using pip
or easy_install), otherwise they will need to be installed manually:
These packages come standard in Python(x,y), Spyder, and other
scientific computing python bundles.
How to install
You have several easy, convenient options to install the mcerp
package (administrative privileges may be required)
Simply copy the unzipped mcerp-XYZ directory to any other location that
python can find it and rename it mcerp.
From the command-line, do one of the following:
Manually download the package files below, unzip to any directory, and
$ [sudo] python setup.py install
If setuptools is installed, run:
$ [sudo] easy_install [--upgrade] mcerp
If pip is installed, run:
$ [sudo] pip install [--upgrade] mcerp
To use this package with Python 3.x, you will need to run the 2to3
conversion tool at the command-line using the following syntax while in the
unzipped mcerp directory:
$ 2to3 -w .
This should take care of the main changes required. Then, run:
$ python3 setup.py install
If bugs continue to pop up, please email the author.
You can also get the bleeding-edge code from GitHub (though I can’t
promise there won’t be stability issues…).