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Set::Partition takes a list or hash of elements and a list numbers that represent the sizes of the partitions into which the list of elements should be arranged. The resulting object can then be used as an iterator which returns a reference to an array of lists, that represents the original list arranged according to the given partitioning. All possible arrangements are returned, and the object returns undef when the entire combination space has been exhausted.

primegen is a small, fast library to generate prime numbers in order. It generates the 50847534 primes up to 1000000000 in just 8 seconds on a Pentium II-350; it prints them in decimal in just 35 seconds. primegen can generate primes up to 1000000000000000, although it is not optimized for primes past 32 bits. It uses the Sieve of Atkin instead of the traditional Sieve of Eratosthenes.

The Statistics::Basic Perl module provides a number of very basic statistical parameters, including the mean, the median, the standard deviation etc. It is reportedly faster than a similar module, Statistics::Descriptive.

Suppose you flip a coin 100 times, and it turns up heads 70 times. Is the coin fair? Suppose you roll a die 100 times, and it shows 30 sixes. Is the die loaded? In statistics, the chi-square test calculates "how random" a series of numbers is. But it doesn't simply say "yes" or "no". Instead, it gives you a confidence interval, which sets upper and lower bounds on the likelihood that the variation in your data is due to chance. See the examples below. There's just one function in this module: chisquare(). Instead of returning the bounds on the confidence interval in a tidy little two-element array, it returns an English string. This was a deliberate design choice---many people misinterpret chi-square results, and the string helps clarify the meaning. -Anton <tobez@FreeBSD.org>

The "Statistics::Contingency" class helps you calculate several useful statistical measures based on 2x2 "contingency tables". I use these measures to help judge the results of automatic text categorization experiments, but they are useful in other situations as well. The general usage flow is to tally a whole bunch of results in the "Statistics::Contingency" object, then query that object to obtain the measures you are interested in. When all results have been collected, you can get a report on accuracy, precision, recall, F1, and so on, with both macro-averaging and micro-averaging over categories.

altgraph is a fork of graphlib: a graph (network) package for constructing graphs, BFS and DFS traversals, topological sort, shortest paths, etc. with graphviz output. altgraph includes some additional usage of Python 2.6+ features and enhancements related to modulegraph and macholib.

Map data for the py-basemap port.

This module provides basic functions used in descriptive statistics. It borrows very heavily from Statistics::Descriptive::Full (which is included with Statistics::Descriptive) with one major difference. This module is optimized for discretized data e.g. data from an A/D conversion that has a discrete set of possible values. E.g. if your data is produced by an 8 bit A/D then you'd have only 256 possible values in your data set. Even though you might have a million data points, you'd only have 256 different values in those million points. Instead of storing the entire data set as Statistics::Descriptive does, this module only stores the values it's seen and the number of times it's seen each value. For very large data sets, this storage method results in significant speed and memory improvements. In a test case with 2.6 million data points from a real world application, Statistics::Descriptive::Discrete took 40 seconds to calculate a set of statistics instead of the 561 seconds required by Statistics::Descriptive::Full. It also required only 4MB of RAM instead of the 400MB used by Statistics::Descriptive::Full for the same data set.

Statistics::Frequency is a simple class for counting elements, in other words, their frequencies. The goal of Statistics::Frequency is simply to be provide container for sets of elements and their respective frequencies.

The Statistics::Lite module is a lightweight, functional alternative to larger, more complete, object-oriented statistics packages. As such, it is likely to be better suited, in general, to smaller data sets.