@RISK Vs R. Monte Carlo simulation in R? Are there any cheaper or open source alternatives to @RISK or are there packages for R that would be able to perform the same tasks?

http://www.palisade.com/risk/

@RISK (pronounced “at risk”) performs risk analysisusing Monte Carlo simulation to show you many possible outcomes in your spreadsheet model—and tells you how likely they are to occur. It mathematically and objectively computes and tracks many different possible future scenarios, then tells you the probabilities and risks associated with each different one. This means you can judge which risks to take and which ones to avoid, allowing for the best decision making under uncertainty. (Taken from their website)

My main purposes for it would be for the construction company I currently work for:

 Able to provide more accurate models for job costing, e.g. time to construct certain parts, associated costs, etc. 

 Provide more accurate models in regard to advertising and marketing, allowing us to better understand and predict what provides the better return. 


If this isn't the right forum for this question, please tell me where it needs to be asked. 

Thank you,
Monte Carlo simulations are very easy in R. The simplest approach is to write your own scripts that carry out the steps you need for your simulations. To construct these scripts you will need to understand what you are simulating, that is what is the distribution of outcomes, and what are you measuring about those outcomes. If you understand what you are simulating than writing these scripts is easy, but will require programming in R. If you are new to R and/or to programming then it will take some effort to get up to speed. In the end, you will probably understand your data, simulations, and results better. There are packages for Monte Carlo in R, but these are not packages that will do the simulations for you and would require as significant learning of R to be able to use (as much as writing your own scripts). 

I suggest reading more about conducting Monte Carlo simulations in R. There are many resources online and offline, such as this book or this tutorial.