This issue of the Journal of Functional Programming (JFP) marks a point of transition. After serving since 1991 as Editor, then since 2004 as co-Editor in Chief (along with Greg Morrisett from 2004 to 2006 and Xavier Leroy since 2007), Paul Hudak is stepping down.

Macros still haven't made their way into typed higher-order programming languages such as Haskell and Standard ML. Therefore, to extend the expressiveness of Haskell or Standard ML, one must express new linguistic features in terms of functions that fit within the static type systems of these languages. This is particularly challenging when introducing features that span across multiple types and that bind variables. We address this challenge by developing, in a step by step manner, mechanisms for encoding patterns and pattern matching in Haskell in a type-safe way.

One approach to give semantics to languages with subtypes is by translation to target languages without subtyping: subtypings A ≤ B are interpreted via conversion functions A → B. This paper shows how to extend the method to languages with computational effects, using Moggi's computational metalanguage.

Regular-expression derivatives are an old, but elegant, technique for compiling regular expressions to deterministic finite-state machines. It easily supports extending the regular-expression operators with boolean operations, such as intersection and complement. Unfortunately, this technique has been lost in the sands of time and few computer scientists are aware of it. In this paper, we reexamine regular-expression derivatives and report on our experiences in the context of two different functional-language implementations. The basic implementation is simple and we show how to extend it to handle large character sets (e.g., Unicode). We also show that the derivatives approach leads to smaller state machines than the traditional algorithm given by McNaughton and Yamada.

Pure pattern calculus supports pattern-matching functions in which patterns are first-class citizens that can be passed as parameters, evaluated and returned as results. This new expressive power supports two new forms of polymorphism. Path polymorphism allows recursive functions to traverse arbitrary data structures. Pattern polymorphism allows patterns to be treated as parameters which may be collected from various sources or generated from training data. A general framework for pattern calculi is developed. It supports a proof of confluence that is parameterised by the nature of the matching algorithm, suitable for the pure pattern calculus and all other known pattern calculi.

The worker/wrapper transformation is a technique for changing the type of a computation, usually with the aim of improving its performance. It has been used by compiler writers for many years, but the technique is little known in the wider functional programming community, and has never been described precisely. In this article we explain, formalise and explore the generality of the worker/wrapper transformation. We also provide a systematic recipe for its use as an equational reasoning technique for improving the performance of programs, and illustrate the power of this recipe using a range of examples.

There is a minor error in Section 3 wherein it is stated that ∖acc 0_ _ k loops in_nitely, even if k succeeds on input _.” This statement is not correct, and should be replaced by ∖If k returns false on input cs, then acc 1_ cs k loops in_nitely.”

The author is grateful to Derek Dreyer for pointing out this mistake, and suggesting the above-mentioned correction.