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General is the default package registry for the Julia programming language, providing the foundation for Julia’s package manager, Pkg.jl. It stores essential information about packages, including versions, dependencies, and compatibility constraints, and serves as the central hub for the Julia package ecosystem. The registry is open to all and makes it easy for developers and researchers to access, install, and share packages across a wide range of domains. New packages and updates are added...

A command line interface (CLI) to Pkg, Julia's package manager.

This is a simple and lightweight development web-server written in Julia, based on HTTP.jl. It has live-reload capability, i.e. when modifying a file, every browser (tab) currently displaying the corresponding page is automatically refreshed.

...BinaryBuilder follows a philosophy that is similar to that of building Julia itself; when you want something done right, you do it yourself. To that end, BinaryBuilder is designed from the ground up to facilitate the building of packages within an easily reproducible and reliable Linux environment, ensuring that the built libraries and executables are deployable to every platform that Julia itself will run on. Packages are cross-compiled using a sequence of shell commands, packaged up inside tarballs, and hosted online for all to enjoy. Package installation is merely downloading, verifying package integrity and extracting that tarball on the user's computer. ...

...The package is aimed to ease the typical steps of analysis of climate models outputs and gridded datasets (support for weather stations is a work-in-progress). Climate indices and bias correction functions are coded to leverage the use of multiple threads. To gain maximum performance, use (bash shell Linux/MacOSX) export JULIA_NUM_THREADS=n, where n is the number of threads. To get an idea of the number of threads you can use type (in Julia) Sys.THREADS. This is especially useful for bias correction.

MLPACK is a C++ machine learning library with 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 flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack