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Java's syntactic and library support for these components tends to cause the above concerns to be mixed with each other, with other concerns, and with low-level implementation details. This means that the code is cluttered, verbose and does not express intent very well. Jedi aims to repair this by providing a library of routines for filtering, iteration, conversion from one collection type to another, first order logic functions, etc. It makes extensive use of higher order functions and closures (also known as 'blocks' or 'lambdas' that can be implemented manually or by annotations.

Class for building pace regression linear models and using them for prediction. Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity. It consists of a group of estimators that are either overall optimal or optimal under certain conditions. The current work of the pace regression theory, and therefore also this implementation, do not handle: - missing values - non-binary nominal attributes - the case that n - k is small where n is the number of instances and k is the number of coefficients (the threshold used in this implmentation is 20) For more information see: Wang, Y (2000). A new approach to fitting linear models in high dimensional spaces. Hamilton, New Zealand. Wang, Y., Witten, I. H.: Modeling for optimal probability prediction. In: Proceedings of the Nineteenth International Conference in Machine Learning, Sydney, Australia, 650-657, 2002.