ParseGenBank is a minimalistic, incremental, event-driven GenBank Flat File Format parser. It aims to efficiently produce events for keywords, features, qualifiers, and coding data. It does not attempt to parse the contents of feature or qualifier data, but provides a framework on which such a system can be built.
PCP (Pattern Classification Program) is a machine learning program for supervised classification of patterns. It runs in interactive and batch modes, and implements the following machine learning algorithms and methods: k-means clustering, Fisher's linear discriminant, dimension reduction using Singular Value Decomposition, Principal Component Analysis, feature subset selection, Bayes error estimation, parametric classifiers (linear and quadratic), pseudo-inverse linear discriminant, k-Nearest Neighbor method, neural networks, Support Vector Machine algorithm (SVM), model selection for SVM, cross-validation, and bagging (committee) classification.
The libmba package is a collection of mostly independent C modules potentially useful to any project. There are the usual ADTs including a linkedlist, hashmap, pool, stack, and varray, a flexible memory allocator, CSV parser, path canonicalization routine, I18N text abstraction, configuration file module, portable semaphores, condition variables, and more. The code is designed so that individual modules can be integrated into existing codebases rather than requiring the user to commit to the entire library. The code has no typedefs, few comments, and extensive man pages and HTML documentation.