Java Grinder takes Java byte-code from a class file and compiles it into an assembly code text file that can be assembled and run on microcontrollers and CPUs including MSP430, dsPIC, 6502 (Commodore 64), 68000, ARM, and MIPS. A Java API is provided for dealing with SPI, GPIO, Commodore 64 hardware, and more.
vimpc is a curses-based client for the Music Player Daemon (mpd) that draws inspiraton from vi/vim. It supports a vim-like rc file, mappable key bindings, normal/ex modes, counts, searching, a music library, directory navigation, playlist editing, and comprehensive help, is highly configurable and customizable, and more. It is written with the intent to make vi users feel at home when using mpd, but is also intuitive and very usable by non-vi users.
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.
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.