Browser-Based Risk Modeling

Risk Modeling with Embedded Spreadsheets


Introducing TREB: Fully Customizable Browser-Based Risk Modeling


TREB is our new embedded spreadsheet applicaton that lets you to build full-featured, customizable risk models right in the browser. TREB works in all browsers and spreadsheets can be embedded in any web page.

Click the “Play” button to run a Monte Carlo simulation:


All you need is a web browser. TREB supports all modern web browsers and has full support for IE11 (although newer browsers will be faster). Just like RiskAMP in Excel, with TREB you can build a simulation model in a spreadsheet, run the Monte Carlo simulation, create and view charts, get statistics, and create distribution tables.

Because it’s browser-based, TREB runs on Windows, OSX, and Linux (including Chromebooks).

All the random distribution function we provide in Excel and all the statistics and analytics functions are available in the browser (actually some may be missing in this release, but they’re all in progress).


We use TREB throughout our website. Check out some examples in these pages:

Walthrough
How to build risk models with RiskAMP (and with TREB)

Multivariate Distributions
Using correlated multivariate distrubutions

The PERT Distribution
Introduction to the PERT distribution, used for modeling expert data (predictions)


Or visit the TREB home page for more.


Additional Features

In addition to what’s available on the web here, custom applications built with TREB and our web risk libraries include:

  • Multi-user editing over local networks or over the web, in real time with full change history and attribution

  • “Presentation Mode”, where you can make changes or run simulations and others can view them in real time

  • Network file storage and management

  • Custom functions and script libraries and integration with third-party tools

  • Theming, custom layout and design

  • Desktop application deployment and support


Contact us for more information on TREB and our browser-based stochastic modeling tools.