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SFST is a toolbox for the implementation of morphological analysers and other tools which are based on finite state transducer technology. The SFST tools comprise: -- a compiler which translates transducer programs into minimised transducers -- interactive and batch-mode analysis programs -- tools for comparing and printing transducers -- an efficient C++ transducer library Features: -- easy to learn for users who are familiar with grep, sed, or Perl. -- efficient implementation in C++ -- supports -- a wide range of transducer operations -- UTF-8 character coding -- weighted transducers (basic functionality only)

A Python wrapper to Qhull (www.qhull.org) for the computation of the convex hull, Delaunay triangulation and Voronoi diagram.

This is a port of Phil Karn's SIMD assisted Viterbi CODEC library. This package may be useful to programmers working on data communications software.

Bayesian estimation, particularly using Markov chain Monte Carlo (MCMC), is an increasingly relevant approach to statistical estimation. However, few statistical software packages implement MCMC samplers, and they are non-trivial to code by hand. pymc is a python package that implements the Metropolis-Hastings algorithm as a python class, and is extremely flexible and applicable to a large suite of problems. pymc includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics.

PySparse extends the Python interpreter by a set of sparse matrix types holding double precision values. PySparse also includes modules that implement: - iterative methods for solving linear systems of equations - a set of standard preconditioners - an interface to a direct solver for sparse linear systems of equations (SuperLU) - a Jacobi-Davidson eigenvalue solver for the symmetric, generalised matrix eigenvalue problem (JDSYM)

PyVTK provides the following tools for manipulating Visualization Toolkit (VTK) files in Python: * ascii and binary output, ascii input from VTK file * DataSet formats: StructuredPoints, StructuredGrid, RectilinearGrid, PolyData, UnstructuredGrid * Data formats: PointData, CellData * DataSetAttr formats: Scalars, ColorScalars, LookupTable, Vectors, Normals, TextureCoordinates, Tensors, Field

ScientificPython is a collection of Python modules that are useful for scientific computing. In this collection you will find modules that cover basic geometry (vectors, tensors, transformations, vector and tensor fields), quaternions, automatic derivatives, (linear) interpolation, polynomials, elementary statistics, nonlinear least-squares fits, unit calculations, Fortran-compatible text formatting, 3D visualization via VRML, and two Tk widgets for simple line plots and 3D wireframe models.

SpeedCrunch is a multiplatform desktop calculator for power users. It is designed to be enjoyed using keyboard. Result is shown in scrollable display, history of expressions is available with up and down arrow. Some other features: optional keypad, syntax highlight, matched parenthesis indicator, just-in-time calculation (show result even before you finish typing) and autocomplete for variables.

Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models.