Standard ML

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Standard ML

(SML) Originally an attempt by Robin Milner <> ca. 1984 to unify the dialects of ML, SML has evolved into a robust general-purpose language. Later versions have been maintained by D. B. MacQueen, Lal George <>, and J. H. Reppy <> at AT&T, and A. W. Appel <>.

SML is functional, with imperative programming features. It is environment based and strict. It adds to ML the call-by-pattern of Hope, recursive data types, reference types, typed exceptions, and modules. (The "core" language excludes the modules).

Standard ML is polymorphically typed and its module system supports flexible yet secure large-scale programming. Standard ML of New Jersey is an optimising native-code compiler for Standard ML that is written in Standard ML. It runs on a wide range of architectures. The distribution also contains: an extensive library - The Standard ML of New Jersey Library, including detailed documentation; Concurrent ML (CML); eXene - an elegant interface to X11 (based on CML); SourceGroup - a separate compilation and "make" facility.

Implementations: SML/NJ, POPLOG ML, Poly/ML, Edinburgh SML, ANU ML, Micro ML, lazy sml2c.

sml2c compiles to C. See also ML Kit.

Version 0.93 runs on 68000, SPARC, MIPS, HPPA, RS/6000, Intel 386, Intel 486 and Macintosh.


FTP from ATT. FTP from Suny SB.

Mailing list:

["A Proposal for Standard ML", R. Milner, ACM Symp on LISP and Functional Prog 1984, pp. 184-197].
References in periodicals archive ?
Set off in comfort mode, it feels like a standard ML, though a loud one.
CDKML's refined classifier showed the best performance in the fall-detection domain where it considerably outperformed all five ML algorithms, the posture-recognition domain followed, while it did not show improvement in comparison to standard ML in the behavioral-cloning domain.
Conclusion: Treatment of pressed mistletoe juice with N-acetyl-galactosamine prior to lectin determination avoids or at least strongly reduces the interference of ML II and III in standard ML I assays (ELLA) and thus provides a simple method to evaluate the content of ML 1 more exactly.

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