This post is a bit of a digression from previous specific software tool discussions, but interesting nonetheless since so many people don’t really think of Decision Support and Analysis as an area of study. Decision Analysis is a broad area of study. The fields of Statistics, Economics, Operations Research and Psychology(to name a few) have contributed many facets of foundational understanding and contributions to real world complex application settings. DAWeb is the web site of the Decision Analysis Society. With regard to software, the various links offer numerous options for those interested in specialized offerings.
During the 1980s Rubin, Little and others established the statistical foundations of Missing Data problems. A Bayesian statistical justification for Multiple Imputation methods provided a principled approach to “fill in” missing data and pooling estimates across solutions based on completed data. NORM by Joseph Schafer
is a late 1990′s easy to use Windows based program that implements the methods of Rubin and Little. A major benefit of NORM is ease of use and the author’s excellent commentary concerning guidance and theoretical contributions. One downside is that for some workflow styles the interface becomes a burden. In addition, the educational benefit of NORM is not to be overlooked. One the other hand, NORM, SAS and STATA have incorporated missing data routines that are better integrated with their other statistical models/methods. This aspect reduces the burden of use in a more general data analysis setting.
DEW is a web based program to help plan Design of Experiments(DOE) designs. Currently block designs, general factorial designs, response surface designs, and more are available. Some output is available as cut-n-paste tables, other options include R code or GenStat sample code. There are many programs devoted to DOE, DEW is easy to use and may meet some of your needs. On the other hand, if you have access to SAS several DOE programs are available. The SAS/STAT product has routine Proc Plan and the SAS/QC product offers Proc Factex and Proc Optex. In addition, there is a SAS GUI interface to this functionality called ADX. Collectively these offer additional features not found in DEW.
MNP is a useful R program for modeling discrete choices, such as choosing among a finite number of alternatives. What makes this an interesting alternative to software such as Stata or Limdep is that model parameters are estimated via Bayesian MonteCarlo Markov Chain(MCMC) methods. Covariates are allowed and control over MCMC tuning is provided. Predictions under a model are available via the posterior predictive distribution.
The word ‘copula’ originates from the Latin noun for a “link or tie” that connects two different things. Over the last decade or so, Copulas have found a niche in Economics and Finance for risk modeling of complex bivariate relationships. More broadly, these models can address structural dependencies in joint distributions that are rather surprising and useful. Matlab has a nice tool to explore these matters. Check out:
http://www.mathworks.com/products/demos/statistics/copulademo.html. Alternatively, more specialized programs to deal with multivariate copulas and maximum likelihood estimation can be found in the R programs: copula, fgac, mlCopulaSlection, and msgcop.