Abstract Haskell's popularity has driven the need for ever more expressive type system features, most of which threaten the decidability and practicality of Damas-Milner type inference. One such feature is the ability to write functions with higher-rank types – that is, functions that take polymorphic functions as their arguments. Complete type inference is known to be undecidable for higher-rank (impredicative) type systems, but in practice programmers are more than willing to add type annotations to guide the type inference engine, and to document their code. However, the choice of just what annotations are required, and what changes are required in the type system and its inference algorithm, has been an ongoing topic of research. We take as our starting point a λ-calculus proposed by Odersky and Läufer. Their system supports arbitrary-rank polymorphism through the exploitation of type annotations on λ-bound arguments and arbitrary sub-terms. Though elegant, and more convenient than some other proposals, Odersky and Läufer's system requires many annotations. We show how to use local type inference (invented by Pierce and Turner) to greatly reduce the annotation burden, to the point where higher-rank types become eminently usable. Higher-rank types have a very modest impact on type inference. We substantiate this claim in a very concrete way, by presenting a complete type-inference engine, written in Haskell, for a traditional Damas-Milner type system, and then showing how to extend it for higher-rank types. We write the type-inference engine using a monadic framework: it turns out to be a particularly compelling example of monads in action. The paper is long, but is strongly tutorial in style. Although we use Haskell as our example source language, and our implementation language, much of our work is directly applicable to any ML-like functional language.

Providing quality health care requires access to continuous patient data that developing countries often lack. A panel of medical informatics specialists, clinical human immunodeficiency virus (HIV) specialists, and program managers suggests a minimum data set for supporting the management and monitoring of patients with HIV and their care programs in developing countries. The proposed minimum data set consists of data for registration and scheduling, monitoring and improving practice management, and describing clinical encounters and clinical care. Data should be numeric or coded using standard definitions and minimal free text. To enhance accuracy, efficiency, and availability, data should be recorded electronically by those generating them. Data elements must be sufficiently detailed to support clinical algorithms/guidelines and aggregation into broader categories for consumption by higher level users (e.g., national and international health care agencies). The proposed minimum data set will evolve over time as funding increases, care protocols change, and additional tests and treatments become available for HIV-infected patients in developing countries.

Record calculi use labels to distinguish between the elements of products and sums. This paper presents a novel variation, type-indexed rows, in which labels are discarded and elements are indexed by their type alone. The calculus, λTIR, can express tuples, recursive datatypes, monomorphic records, polymorphic extensible records, and closed-world style type-based overloading. Our motivating application of λTIR, however, is to encode the "choice" types of XML, and the "unordered tuple" types of SGML. Indeed, λTIR is the kernel of the language XMλ, a lazy functional language with direct support for XML types ("DTDs") and terms ("documents").The system is built from rows, equality constraints, insertion constraints and constrained, or qualified, parametric polymorphism. The test for constraint satisfaction is complete, and for constraint entailment is only mildly incomplete. We present a type checking algorithm, and show how λTIR may be implemented by a type-directed translation which replaces type-indexing by conventional natural-number indexing. Though not presented in this paper, we have also developed a constraint simplification algorithm and type inference system.

This paper introduces a language feature, called implicit parameters, that provides dynamically scoped variables within a statically-typed Hindley-Milner framework. Implicit parameters are lexically distinct from regular identifiers, and are bound by a special with construct whose scope is dynamic, rather than static as with let. Implicit parameters are treated by the type system as parameters that are not explicitly declared, but are inferred from their use.

Compilers for ML and Haskell use intermediate languages that incorporate deeply-embedded assumptions about order of evaluation and side effects. We propose an intermediate language into which one can compile both ML and Haskell, thereby facilitating the sharing of ideas and infrastructure, and supporting language developments that move each language in the direction of the other. Achieving this goal without compromising the ability to Compile as good code as a more direct route turned out to be much more subtle than we expected. We address this challenge using monads and unpointed types, identify two alternative language designs, and explore the choices they embody.

Dynamic typing extends statically typed languages with a universal datatype, simplifying programs which must manipulate other programs as data, such as distributed, persistent, interpretive and generic programs. Current approaches, however, limit the use of polymorphism in dynamic values, and can be syntactically awkward.We introduce a new approach to dynamic typing, based on staged computation, which allows a single type-reconstruction algorithm to execute partly at compile time and partly at run-time. This approach seamlessly extends a single type system to accommodate types that are only known at run-time, while still supporting both type inference and polymorphism. The system is significantly more expressive than other approaches. Furthermore it can be implemented efficiently; most of the type inference is done at compile-time, leaving only some residual unification for run-time.We demonstrate our approach by examples in a small polymorphic functional language, and present its type system, type reconstruction algorithm, and operational semantics. Our proposal could also be readily adapted to many other programming languages.