The advent of inexpensive, powerful desktop computing is revolutionizing statistical data analysis. Idealized models can be replaced with more realistic modeling, and in many cases, by virtually model-free analysis. The ensuing rewards are highlighted by recent advances in data visualization methods, where a picture can indeed be worth a thousand words, provided the researcher is visually guided as to the distinction between truly important features in the data and noise. Statistical smoothing is a powerful method for finding structure in data and is especially useful for finding unexpected features during the exploratory phase of data analysis. While classical parametric models can often provide statistical insight, their initial application can obscure unanticipated phenomena. The goals of this project are to develop software for implementing statistical data smoothing methods based on scale space visualization representations into an easy-to-use comprehensive statistical software package. Designing the software around a Visual Programming Environment component architecture not only makes data smoothing methods easy to implement but encourages their use and exploration as well. The software will be distributed as a stand-alone package and via the Internet. The wide appeal, applicability, and interpretability make these methods relevant and useful within most of the NIH Institutes.
The commercial potential for computer-intensive, statistical data visualization software is enormous. These methods provide a flexible approach to problems of inference and can successfully handle circumstances where standard approaches are deemed inappropriate or cumbersome. Researchers in any subject needing to take advantage of these new procedures are potential customers of such a statistical software product. The commercial potential is strengthened by making the software widely and easily available via the Internet.
