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.
Libfbm is a C++ library for fast and accurate bulk-simulation of multi-dimensional (1D, 2D, 3D, .., 8D) Gaussian stationary processes, fractional Brownian motion, and fields with power-law power spectrum. It makes use of the circulant matrix embedding and FFT. Random number generation is provided by SFMT (SIMD-optimized Mersenne Twister) with a ziggurat based algorithm for normal distribution. For FFT functions, it depends on the FFTW library.
HEALPix is a set of scientific tools implementing the Hierarchical Equal Area isoLatitude Pixelation of the sphere. As suggested in the name, this pixelation produces a subdivision of a spherical surface in which every single pixel covers the same surface area. HEALPix provides various programs and libraries in C, C++, Fortran, GDL/IDL, Java, and Python which facilitate discretization, simulation, processing, analysis, and visualization of data on the sphere up to very high resolution. It is the state-of-the-art program used in astronomy and cosmology to deal with massive full-sky data sets.
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.