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Lua is a programming language originally designed for extending applications, but also frequently used as a general-purpose, stand-alone language. Lua combines simple procedural syntax (similar to Pascal) with powerful data description constructs based on associative arrays and extensible semantics. Lua is dynamically typed, interpreted from bytecodes, and has automatic memory management with garbage collection, making it ideal for configuration, scripting, and rapid prototyping. A fundamental concept in the design of Lua is to provide meta-mechanisms for implementing features, instead of providing a host of features directly in the language. For example, although Lua is not a pure object-oriented language, it does provide meta-mechanisms for implementing classes and inheritance. Lua's meta-mechanisms bring an economy of concepts and keep the language small, while allowing the semantics to be extended in unconventional ways. Extensible semantics is a distinguishing feature of Lua. Lua is implemented as a small library of C functions, written in ANSI C, and compiles unmodified in all known platforms. The implementation goals are simplicity, efficiency, portability, and low embedding cost.

Lua is a programming language originally designed for extending applications, but also frequently used as a general-purpose, stand-alone language. Lua combines simple procedural syntax (similar to Pascal) with powerful data description constructs based on associative arrays and extensible semantics. Lua is dynamically typed, interpreted from bytecodes, and has automatic memory management with garbage collection, making it ideal for configuration, scripting, and rapid prototyping. A fundamental concept in the design of Lua is to provide meta-mechanisms for implementing features, instead of providing a host of features directly in the language. For example, although Lua is not a pure object-oriented language, it does provide meta-mechanisms for implementing classes and inheritance. Lua's meta-mechanisms bring an economy of concepts and keep the language small, while allowing the semantics to be extended in unconventional ways. Extensible semantics is a distinguishing feature of Lua. Lua is implemented as a small library of C functions, written in ANSI C, and compiles unmodified in all known platforms. The implementation goals are simplicity, efficiency, portability, and low embedding cost.

MIX is Donald Knuth's mythical computer as described in his monumental work The Art Of Computer Programming. As any of its real counterparts, the MIX features registers, memory cells, an overflow toggle, comparison flags, input-output devices, and a set of binary instructions executable by its virtual CPU. You can programme the MIX using an assembly language called MIXAL, the MIX Assembly Language. The MIX Development Kit offers an emulation of MIX and MIXAL. The current version of MDK includes the following applications: - mixasm A MIXAL compiler, which translates your source files into binary ones, executable by the MIX virtual machine. - mixvm A MIX virtual machine which is able to run and debug compiled MIXAL programs, using a command line interface with readline's line editting capabilities. - gmixvm A MIX virtual machine with a GTK+ GUI which allows you running and debugging your MIXAL programs through a nice graphical interface. - mixvm.el An elisp program which allows you to run mixvm within an Emacs GUD window, simultaneously viewing your MIXAL source file in another buffer.

P2c is a tool for translating Pascal programs into C. The input consists of a set of source files in any of the following Pascal dialects: HP Pascal, Turbo/UCSD Pascal, DEC VAX Pascal, Oregon Software Pascal/2, Macintosh Programmer's Workshop Pascal, Sun/Berkeley Pascal, Texas Instruments Pascal, Apollo Domain Pascal. Modula-2 syntax is also supported. Output is a set of .c and .h files that comprise an equivalent program in any of several dialects of C. Output code may be kept machine- and dialect independent, or it may be targeted to a specific machine and compiler. Most reasonable Pascal programs are converted into fully functional C which will compile and run with no further modifications, although p2c sometimes chooses to generate readable code at the expense of absolute generality. P2c endeavors to insert notes and warning messages into the output code to point out areas which may require human intervention. Output code is arranged to be readable and efficient, and to make use of C idioms wherever possible. The main goal of the translation is to produce C files which are pleasant and "natural" enough to be acceptable as the new source files for a program. In a pinch, p2c will also serve as an ad hoc Pascal compiler. The p2cc script makes it easy to use p2c as a compiler.

This module was created as a low-level inteface to any IMAP server. It was built to be a 'clear box' solution to working with an IMAP environment. The idea is that anything an IMAP client should be able to do, and any information available via the IMAP specs, should be available to a client interface and user. This way, the full strength of the IMAP protocol and data can be utilized, ideally in the most network-efficient mannger possible, rather than being contrained only to a subset of commands or data-limited responses. If the server says it, the client should be able to see it. This module also takes steps to be able to handle anticipated situations for the user rather than forcing a per-implementation behavior for such expected events, such as referrals. IMAP::Client will fully support referrals, and will transparently handle them for whatever command is issued to them (so long as the referral s for anonymous or the same user with the same password - a new user or different password would require a new username/password to be obtained. As of 0.01, this is not supported, however the framework is down.

Before a calculation can be performed on a parallel computer, it must first be decomposed into tasks which are assigned to different processors. Efficient use of the machine requires that each processor have about the same amount of work to do and that the quantity of interprocessor communication is kept small. Finding an optimal decomposition is provably hard, but due to its practical importance, a great deal of effort has been devoted to developing heuristics for this problem. The decomposition problem can be addressed in terms of graph partitioning. Chaco implements a variety of algorithms for graph partitioning and is used at most of the major parallel computing centers around the world to simplify the development of parallel applications, and to ensure that high performance is obtained. Chaco has contributed to a wide variety of computational studies including investigation of the molecular structure of liquid crystals, evaluating the design of a chemical vapor deposition reactor and modeling automobile collisions. Note: this port includes a patch provided by Walter Landry for use within MBDyn.

At the suggestion of Linas Vepstas on the Gnu Scientific Library (GSL) list, this GPL'd suite of random number tests will be named "Dieharder". Using a movie sequel pun for the name is a double tribute to George Marsaglia, whose "Diehard battery of tests" of random number generators has enjoyed years of enduring usefulness as a test suite. The dieharder suite is more than just the diehard tests cleaned up and given a pretty GPL'd source face in native C: tests from the Statistical Test Suite (STS) developed by the National Institute for Standards and Technology (NIST) are being incorporated, as are new tests developed by rgb. Where possible, tests are parametrized and controllable so that failure, at least, is unambiguous. A further design goal is to provide some indication of *why* a generator fails a test, where such information can be extracted during the test process and placed in usable form. For example, the bit-distribution tests should (eventually) be able to display the actual histogram for the different bit n-tuplets. Dieharder is by design extensible. It is intended to be the "Swiss army knife of random number test suites", or if you prefer, "the last suite you'll ever ware" for testing random numbers.

Math::Sequence defines a class for simple mathematic sequences with a recursive definition such as x_(n+1) = 1 / (x_n + 1). Creation of a Math::Sequence object is described below in the paragraph about the constructor. Math::Sequence uses Math::Symbolic to parse and modify the recursive sequence definitions. That means you specify the sequence as a string which is parsed by Math::Symbolic. Alternatively, you can pass the constructor a Math::Symbolic tree directly. Because Math::Sequence uses Math::Symbolic for its implementation, all results will be Math::Symbolic objects which may contain other variables than the sequence variable itself. Each Math::Sequence object is an iterator to iterate over the elements of the sequence starting at the first element (which was specified by the starting element, the second argument to the new() constructor). It offers facilities to cache all calculated elements and access any element directly, though unless the element has been cached in a previous calculation, this is just a shortcut for repeated use of the iterator.

TestU01 is a software library, implemented in the ANSI C language, and offering a collection of utilities for the empirical statistical testing of uniform random number generators. The library implements several types of random number generators in generic form, as well as many specific generators proposed in the literature or found in widely-used software. It provides general implementations of the classical statistical tests for random number generators, as well as several others proposed in the literature, and some original ones. These tests can be applied to the generators predefined in the library and to user-defined generators. Specific tests suites for either sequences of uniform random numbers in [0,1] or bit sequences are also available. Basic tools for plotting vectors of points produced by generators are provided as well. Additional software permits one to perform systematic studies of the interaction between a specific test and the structure of the point sets produced by a given family of random number generators. That is, for a given kind of test and a given class of random number generators, to determine how large should be the sample size of the test, as a function of the generator's period length, before the generator starts to fail the test systematically.

LyX is a document processor that encourages an approach to writing based on the structure of your documents (WYSIWYM) and not simply their appearance (WYSIWYG). LyX combines the power and flexibility of TeX/LaTeX with the ease of use of a graphical interface. This results in world-class support for creation of mathematical content (via a fully integrated equation editor) and structured documents like academic articles, theses, and books. In addition, staples of scientific authoring such as reference list and index creation come standard. But you can also use LyX to create a letter or a novel or a theatre play or film script. A broad array of ready, well-designed document layouts are built in. LyX is for people who want their writing to look great, right out of the box. No more endless tinkering with formatting details, "finger painting" font attributes or futzing around with page boundaries. You just write. On screen, LyX looks like any word processor; its printed output - or richly cross-referenced PDF, just as readily produced - looks like nothing else.