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Foomatic is a database-driven system for integrating free software printer drivers with common spoolers under Unix. It supports CUPS, LPRng, LPD, GNUlpr, Solaris LP, PPR, PDQ, CPS, and direct printing with every free software printer driver known to us and every printer known to work with these drivers.

ps2eps is a tool (written in Perl) to produce Encapsulated PostScript Files (EPS/EPSF) from usual one-paged Postscript documents. It calculates correct Bounding Boxes for those EPS files and filters some special postscript command sequences that can produce erroneous results on printers.

Tgif2tex allows us to use LaTeX commands in figures drawn by Tgif. It extracts strings and their positions from the figure and converts it in picture environment of the LaTeX. It also converts other components of the figure such as lines, circles, ovals, etc into EPS.

xtexsh - xTeX Shell by Gerald Teschlxtem The present program is a simple TeX interface for the X Window System based on "wish", respectively Tcl/Tk.

pkg_search queries the appropriate database file of FreeBSD, DragonFlyBSD or NetBSD for a given package name.

Xcode automatically determines input file charset and converts it to the necessary charset. The important feature of the program is biunique charset conversion which protects your file from damages.

FastCap computes self and mutual capacitances between ideal conductors of arbitrary shapes, orientations and sizes. The conductors can be embedded in a dielectric region composed of any number of constant-permittivity regions of any shape and size. The algorithm used in FastCap is an acceleration of the boundary-element technique for solving the integral equation associated with the multiple-conductor, multiple-dielectric capacitance extraction problem. The linear system resulting from the boundary-element discretization is solved using a generalized conjugate residual algorithm with a fast multipole algorithm to efficiently compute the iterates. --------------------- This version of fastcap has been cleaned up and enhanced by Stephen R. Whiteley of Whitleley Research Inc. ---------------------

LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized classifiers (L2-loss linear SVM, L1-loss linear SVM, and logistic regression), L1-regularized classifiers (L2-loss linear SVM and logistic regression). Main features of LIBLINEAR include - Same data format as LIBSVM and similar usage - One-vs-the rest and Crammer & Singer multi-class classification - Cross validation for model selection - Probability estimates (logistic regression only) - Weights for unbalanced data

This slave port adds Python interface to LIBSVM.

LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification. Since version 2.8, it implements an SMO-type algorithm proposed in this paper: R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there. Our goal is to help users from other fields to easily use SVM as a tool. LIBSVM provides a simple interface where users can easily link it with their own programs. Main features of LIBSVM include * Different SVM formulations * Efficient multi-class classification * Cross validation for model selection * Probability estimates * Weighted SVM for unbalanced data * Both C++ and Java sources * GUI demonstrating SVM classification and regression * Python, R (also Splus), MATLAB, Perl, Ruby, Weka, Common LISP and LabVIEW interfaces. C# .NET code is available. It's also included in some learning environments: YALE and PCP. * Automatic model selection which can generate contour of cross valiation accuracy.