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.
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.
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.
The program arbtt, the automatic rule-based time tracker, allows you to investigate how you spend your time, without having to manually specify what you are doing. arbtt records which windows are open and active, and provides you with a powerful rule-based language to afterwards categorize your work.
n2 is a client/server system for transmitting forensic snapshots from a number of hosts to a receiver node. This receiver collects statistics and is able to present an overview of the current and historical situation on a server. n2 provides a robust solution for real-time monitoring, optimizing performance, and analyzing crashes.
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.
KeyFrog monitors the keyboard and visualizes its usage statistics. The user can obtain much information about keyboard activity: the intensity of keyboard usage, how was it distributed in time, which applications were used, etc. This may be very useful, for example, to developers to monitor their productivity. The environment being monitored is the X Window System (text applications are explicitly supported if run inside an X terminal).