GTKMathplot is an interactive plotting program based on GTK+ and the Cairo graphics library. It can display bidimensional curves, tridimensional curves, and surfaces from Cartesian or parametric equations describing the mathematical objects you would like to visualize. It is designed to be intuitive for high school students with good mathematical knowledge. University students of engineering, physics, mathematics, or natural sciences should need no explanation at all to start using it proficiently.
LibBi is used for state-space modelling and Bayesian inference on high-performance computer hardware, including multi-core CPUs, many-core GPUs (graphics processing units), and distributed-memory clusters. The staple methods of LibBi are based on sequential Monte Carlo (SMC), also known as particle filtering. These methods include particle Markov chain Monte Carlo (PMCMC) and SMC2. Other methods include the extended Kalman filter and some parameter optimization routines. LibBi consists of a C++ template library and a parser and compiler, written in Perl, for its own modelling language.
MLPACK is a C++ machine learning library with an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. It contains algorithms such as k-means, Gaussian mixture models, hidden Markov models, density estimation trees, kernel PCA, locality-sensitive hashing, sparse coding, linear regression and least-angle regression.
libefgy is a set of C++ headers containing lots of templates loosely related to maths. The headers include templates for fractional arithmetic, big integers (and thus "big fractions"), calculating π, e, and some calculations with those (for trigonometrics), matrix manipulations, tuples, polar and Euclidian spaces in arbitrary dimensions, (perspective) projections, colour space manipulations in RGB and HSL, and assorted other things.
The Graphical Models Toolkit (GMTK) is a toolkit for rapidly prototyping statistical models using dynamic graphical models (DGMs) and dynamic Bayesian networks (DBNs). It can be used for speech and language processing, bioinformatics, activity recognition, and any time series application. It features exact and approximate inference, many built-in factors including dense, sparse, and deterministic conditional probability tables, native support for ARPA backoff-based factors and factored language models, parameter sharing, gamma and beta distributions, dense and sparse Gaussian factors, heterogeneous mixtures, deep neural network factors, and time-inhomogeneous trellis factors, arbitrary order embedded Markov chains, a GUI graph viewer, and much more.