Julia's package ecosystem is organized in terms of Github organizations. While this is informal, many of the main packages (but not all!) can be found in the various organizations.

http://julialang.org/community/

A useful source on the the changing package ecosystem (might be) found here:

Let's take a quick look at some organizations which provide important functionality to Julia. I will go through some of the most well-developed and "ready for use orgs". Of course, there are more that I will be leaving off the list.

- JuliaLang is the Base organization
- It holds the Julia language itself
- Other core pacakges exist in JuliaLang
- PkgDev for package development
- IJulia
- Compat for version compatibility

- There is a general trend of "slimming Base" to lower the Travis load on JuliaLang

- Hosts Dataframes.jl, the data frame implementation of Julia
- Distributions.jl holds probability distributions and methods for generating random numbers according to specific distributions
- The standard regression and hypothesis testing libraries are held here
- Klara.jl is a native MCMC engine
- One of the main R linear model library developers, Douglas Bates, is a heavy contributor

As some of you may know, I have had a (rather late) mid-life crisis and run off with another language called Julia. (http://julialang.org)

Note, Dataframes used to be slow. A very large change is coming in the next week. To understand it in detail, read: http://www.johnmyleswhite.com/notebook/2015/11/28/why-julias-dataframes-are-still-slow/

- Julia for Mathematical Programming (JuMP) is one of the premire Julia libraries. It implements a DSL for interfacing with many commercial and non-commercial mathematical optimization (linear, mixed-integer, conic, semidefinite, nonlinear) algorithms. Most of JuliaOpt can be used through JuMP
- Optim.jl are a set of native Julia optimization algorithms
- An interesting fact is that the creator of NLopt is a heavy contributor to Julia and JuliaOpt

Bindings to many popular parallel libraries / APIs are found in JuliaParallel:

- DistributedArrays.jl: A distributed array implmentation
- PETSc.jl
- MPI.jl
- ScaLAPACK.jl

Bindings for common GPU libraries:

- ArrayFire.jl
- CUDArt.jl
- CUSPARSE.jl
- CUDNN.jl
- CUFFT.jl
- CUBLAS.jl

JuliaGPU is also developing a framework for easy GPU usage:

- CUDAnative.jl
- GPUArrays.jl

JuliaDiff holds libraries for differentiation in Julia

- ForwardDiff.jl: A robust implementation of forward-mode autodifferentiation
- ReverseDiffSource.jl: A newer library for reverse-mode autodifferentiation (backwards propogation)

JuliaGraphs is built around LightGraphs.jl, a fast and performant implementation of graph algorithms in Julia

JuliaMath holds basic mathematical libraries.

- IterativeSolvers.jl: Iterative methods for
`Ax=b`

, Krylov subspace methods, etc. - Roots.jl: Root-finding algorithms

JuliaDiffEq holds the packages for solving differential equations.

- DifferentialEquations.jl: The core package for solving ODEs, SDEs, PDEs, DAEs, DDEs, jump problems, etc.
- Sundials.jl: Wrappers for the Sundials ODE/DAE solvers

Interoperability of Julia with other languages.

- MATLAB.jl
- RCall.jl
- Mathematica.jl
- JavaCall.jl
- CxxWrap.jl
- ObjectiveC.jl

Julia interop with Python

- PyPlot.jl: A wrapper for the Python matplotlib library
- SymPy.jl
- PyCall.jl
- pyjulia
- Pandas.jl

- JLD.jl: An HDF5-based saving format for Julia
- Bio.jl: A huge library for bioinformatics in Julia