Being able to create insightful visualizations from both simulated and measured data is important for the visualization community. For scalar volumes, direct volume rendering has proved a useful tool for data exploration. Using a transfer function, we can map scalar values to colors and opacities to identify and enhance important features. Although researchers have developed some automatic techniques for transfer-function specification, the exploration process still requires users to tune the parameters manually until they can produce the desired visualization. Researchers have conducted substantial work to assist users in this specification task by providing interactive widgets. These tools generally assist users by letting them create and manipulate widgets over one or more dimensions of histogram information representing the data. The authors' framework combines elements of existing transfer-function specification techniques and introduces new features to handle the direct volume rendering of a diversity of volumetric data sets.