Armadillo is a C++ linear algebra library (matrix maths) aiming towards a good balance between speed and ease of use. The API is deliberately similar to Matlab's. Integer, floating point, and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS numerics libraries. A delayed evaluation approach, based on template meta-programming, is used (during compile time) to combine several operations into one and reduce or eliminate the need for temporaries.
SCaVis is an environment for scientific computation, data analysis, and data visualization designed for scientists, engineers, and students. The program can be used for function and data plotting in 2D and 3D, histograms, statistical analysis, and symbolic calculations using the Matlab/Octave high-level interpreted language.
superseriousstats is a small and efficient program for creating a Web page with statistics from various types of IRC logs. It keeps track of its parse history and only processes new activity before storing any accumulated data in a SQLite or MySQL database. It is suitable for high volume IRC channels and large log archives, and is relatively easy to integrate with IRC services (e.g. bots) that interact with the database and provide last seen information and many other statistics directly in your channel.
SOFA is a statistics, analysis, and reporting program with an emphasis on ease of use, learning as you go, and beautiful output. SOFA can connect directly to your database and lets you display results in an attractive format ready to share or put in a spreadsheet. SOFA will help you learn as you go, whether you are a student, business analyst, or researcher.
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.
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.
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.
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.