APL

• • IJulia - language backend combined with the Jupyter interactive environment

• General-Purpose Machine Learning

  • MachineLearning - Julia Machine Learning library.
  • MLBase - A set of functions to support the development of machine learning algorithms.
  • PGM - A Julia framework for probabilistic graphical models.
  • DA - Julia package for Regularized Discriminant Analysis.
  • Regression - Algorithms for regression analysis (e.g. linear regression and logistic regression).
  • Local Regression - Local regression, so smooooth!.
  • Naive Bayes - Simple Naive Bayes implementation in Julia.
  • Mixed Models - A Julia package for fitting (statistical) mixed-effects models.
  • Simple MCMC - basic mcmc sampler implemented in Julia.
  • Distance - Julia module for Distance evaluation.
  • Decision Tree - Decision Tree Classifier and Regressor.
  • Neural - A neural network in Julia.
  • MCMC - MCMC tools for Julia.
  • Mamba - Markov chain Monte Carlo (MCMC) for Bayesian analysis in Julia.
  • GLM - Generalized linear models in Julia.
  • Gaussian Processes - Julia package for Gaussian processes.
  • Online Learning
  • GLMNet - Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet.
  • Clustering - Basic functions for clustering data: k-means, dp-means, etc.
  • SVM - SVM's for Julia.
  • Kernel Density - Kernel density estimators for julia.
  • Dimensionality Reduction - Methods for dimensionality reduction.
  • NMF - A Julia package for non-negative matrix factorization.
  • ANN - Julia artificial neural networks.
  • Mocha - Deep Learning framework for Julia inspired by Caffe.
  • XGBoost - eXtreme Gradient Boosting Package in Julia.
  • ManifoldLearning - A Julia package for manifold learning and nonlinear dimensionality reduction.
  • MXNet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.
  • Merlin - Flexible Deep Learning Framework in Julia.
  • ROCAnalysis - Receiver Operating Characteristics and functions for evaluation probabilistic binary classifiers.
  • GaussianMixtures - Large scale Gaussian Mixture Models.
  • ScikitLearn - Julia implementation of the scikit-learn API.
  • Knet - Koç University Deep Learning Framework.
  • Topic Models - TopicModels for Julia.
  • Text Analysis - Julia package for text analysis.

• Data Analysis / Data Visualization

  • Graph Layout - Graph layout algorithms in pure Julia.
  • LightGraphs - Graph modeling and analysis.
  • Data Frames Meta - Metaprogramming tools for DataFrames.
  • Julia Data - library for working with tabular data in Julia.
  • Data Read - Read files from Stata, SAS, and SPSS.
  • Hypothesis Tests - Hypothesis tests for Julia.
  • Gadfly - Crafty statistical graphics for Julia.
  • Stats - Statistical tests for Julia.
  • RDataSets - Julia package for loading many of the data sets available in R.
  • DataFrames - library for working with tabular data in Julia.
  • Distributions - A Julia package for probability distributions and associated functions.
  • Data Arrays - Data structures that allow missing values.
  • Time Series - Time series toolkit for Julia.
  • Sampling - Basic sampling algorithms for Julia.

• Misc Stuff / Presentations

  • DSP - Digital Signal Processing (filtering, periodograms, spectrograms, window functions).
  • JuliaCon Presentations - Presentations for JuliaCon.
  • SignalProcessing - Signal Processing tools for Julia.
  • Images - An image library for Julia.