For a list of free machine learning books available for download, go [here](https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.md)
* [Accord-Framework](http://accord-framework.net/) -The Accord.NET Framework is a complete framework for building machine learning, computer vision, computer audition, signal processing and statistical applications.
* [Stan](http://mc-stan.org/) - A probabilistic programming language implementing full Bayesian statistical inference with Hamiltonian Monte Carlo sampling
* [MIT Information Extraction Toolkit](https://github.com/mit-nlp/MITIE) - C, C++, and Python tools for named entity recognition and relation extraction
* [CRF++](http://crfpp.googlecode.com/svn/trunk/doc/index.html) - Open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data & other Natural Language Processing tasks.
* [Kaldi](http://kaldi.sourceforge.net/) - Kaldi is a toolkit for speech recognition written in C++ and licensed under the Apache License v2.0. Kaldi is intended for use by speech recognition researchers.
* [ToPS](https://github.com/ayoshiaki/tops) - This is an objected-oriented framework that facilitates the integration of probabilistic models for sequences over a user defined alphabet.
* [HLearn](https://github.com/mikeizbicki/HLearn) - a suite of libraries for interpreting machine learning models according to their algebraic structure.
* [CoreNLP] (http://nlp.stanford.edu/software/corenlp.shtml) - Stanford CoreNLP provides a set of natural language analysis tools which can take raw English language text input and give the base forms of words
* [Stanford Parser] (http://nlp.stanford.edu/software/lex-parser.shtml) - A natural language parser is a program that works out the grammatical structure of sentences
* [Stanford Name Entity Recognizer] (http://nlp.stanford.edu/software/CRF-NER.shtml) - Stanford NER is a Java implementation of a Named Entity Recognizer.
* [Stanford Word Segmenter] (http://nlp.stanford.edu/software/segmenter.shtml) - Tokenization of raw text is a standard pre-processing step for many NLP tasks.
* [Tregex, Tsurgeon and Semgrex](http://nlp.stanford.edu/software/tregex.shtml) - Tregex is a utility for matching patterns in trees, based on tree relationships and regular expression matches on nodes (the name is short for "tree regular expressions").
* [Stanford Phrasal: A Phrase-Based Translation System](http://nlp.stanford.edu/software/phrasal/)
* [Stanford English Tokenizer](http://nlp.stanford.edu/software/tokenizer.shtml) - Stanford Phrasal is a state-of-the-art statistical phrase-based machine translation system, written in Java.
* [Stanford Tokens Regex](http://nlp.stanford.edu/software/tokensregex.shtml) - A tokenizer divides text into a sequence of tokens, which roughly correspond to "words"
* [Stanford SPIED](http://nlp.stanford.edu/software/patternslearning.shtml) - Learning entities from unlabeled text starting with seed sets using patterns in an iterative fashion
* [Stanford Topic Modeling Toolbox](http://nlp.stanford.edu/software/tmt/tmt-0.4/) - Topic modeling tools to social scientists and others who wish to perform analysis on datasets
* [Twitter Text Java](https://github.com/twitter/twitter-text-java) - A Java implementation of Twitter's text processing library
* [MALLET](http://mallet.cs.umass.edu/) - A Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
* [OpenNLP](https://opennlp.apache.org/) - a machine learning based toolkit for the processing of natural language text.
* [LingPipe](http://alias-i.com/lingpipe/index.html) - A tool kit for processing text using computational linguistics.
* [ClearTK](https://code.google.com/p/cleartk/) - ClearTK provides a framework for developing statistical natural language processing (NLP) components in Java and is built on top of Apache UIMA.
* [Apache cTAKES](http://ctakes.apache.org/) - Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) is an open-source natural language processing system for information extraction from electronic medical record clinical free-text.
* [Datumbox](https://github.com/datumbox/datumbox-framework) - Machine Learning framework for rapid development of Machine Learning and Statistical applications
* [Stanford Classifier](http://nlp.stanford.edu/software/classifier.shtml) - A classifier is a machine learning tool that will take data items and place them into one of k classes.
* [Weka](http://www.cs.waikato.ac.nz/ml/weka/) - Weka is a collection of machine learning algorithms for data mining tasks
* [TextProcessing](https://www.mashape.com/japerk/text-processing/support) - Sentiment analysis, stemming and lemmatization, part-of-speech tagging and chunking, phrase extraction and named entity recognition.
* [cephes](http://jucor.github.io/torch-cephes) - Cephes mathematical functions library, wrapped for Torch. Provides and wraps the 180+ special mathematical functions from the Cephes mathematical library, developed by Stephen L. Moshier. It is used, among many other places, at the heart of SciPy.
* [graph](https://github.com/torch/graph) - Graph package for Torch
* [randomkit](http://jucor.github.io/torch-randomkit/) - Numpy's randomkit, wrapped for Torch
* [signal](http://soumith.ch/torch-signal/signal/) - A signal processing toolbox for Torch-7. FFT, DCT, Hilbert, cepstrums, stft
* [nn](https://github.com/torch/nn) - Neural Network package for Torch
* [nngraph](https://github.com/torch/nngraph) - This package provides graphical computation for nn library in Torch7.
* [nnx](https://github.com/clementfarabet/lua---nnx) - A completely unstable and experimental package that extends Torch's builtin nn library
* [optim](https://github.com/torch/optim) - An optimization library for Torch. SGD, Adagrad, Conjugate-Gradient, LBFGS, RProp and more.
* [unsup](https://github.com/koraykv/unsup) - A package for unsupervised learning in Torch. Provides modules that are compatible with nn (LinearPsd, ConvPsd, AutoEncoder, ...), and self-contained algorithms (k-means, PCA).
* [lbfgs](https://github.com/clementfarabet/lbfgs) - FFI Wrapper for liblbfgs
* [vowpalwabbit](https://github.com/clementfarabet/vowpal_wabbit) - An old vowpalwabbit interface to torch.
* [OpenGM](https://github.com/clementfarabet/lua---opengm) - OpenGM is a C++ library for graphical modeling, and inference. The Lua bindings provide a simple way of describing graphs, from Lua, and then optimizing them with OpenGM.
* [sphagetti](https://github.com/MichaelMathieu/lua---spaghetti) - Spaghetti (sparse linear) module for torch7 by @MichaelMathieu
* [LuaSHKit](https://github.com/ocallaco/LuaSHkit) - A lua wrapper around the Locality sensitive hashing library SHKit
* [kernel smoothing](https://github.com/rlowrance/kernel-smoothers) - KNN, kernel-weighted average, local linear regression smoothers
* [cutorch](https://github.com/torch/cutorch) - Torch CUDA Implementation
* [cunn](https://github.com/torch/cunn) - Torch CUDA Neural Network Implementation
* [imgraph](https://github.com/clementfarabet/lua---imgraph) - An image/graph library for Torch. This package provides routines to construct graphs on images, segment them, build trees out of them, and convert them back to images.
* [videograph](https://github.com/clementfarabet/videograph) - A video/graph library for Torch. This package provides routines to construct graphs on videos, segment them, build trees out of them, and convert them back to videos.
* [saliency](https://github.com/marcoscoffier/torch-saliency) - code and tools around integral images. A library for finding interest points based on fast integral histograms.
* [Contourlets](http://www.ifp.illinois.edu/~minhdo/software/contourlet_toolbox.tar) - MATLAB source code that implements the contourlet transform and its utility functions.
* [Shearlets](http://www.shearlab.org/index_software.html) - MATLAB code for shearlet transform
* [Curvelets](http://www.curvelet.org/software.html) - The Curvelet transform is a higher dimensional generalization of the Wavelet transform designed to represent images at different scales and different angles.
* [Bandlets](http://www.cmap.polytechnique.fr/~peyre/download/) - MATLAB code for bandlet transform
* [t-Distributed Stochastic Neighbor Embedding](http://homepage.tudelft.nl/19j49/t-SNE.html) - t-Distributed Stochastic Neighbor Embedding (t-SNE) is a (prize-winning) technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets.
* [Spider](http://people.kyb.tuebingen.mpg.de/spider/) - The spider is intended to be a complete object orientated environment for machine learning in Matlab.
* [LibSVM](http://www.csie.ntu.edu.tw/~cjlin/libsvm/#matlab) - A Library for Support Vector Machines
* [LibLinear](http://www.csie.ntu.edu.tw/~cjlin/liblinear/#download) - A Library for Large Linear Classification
* [Machine Learning Module](https://github.com/josephmisiti/machine-learning-module) - Class on machine w/ PDF,lectures,code
* [Pattern Recognition and Machine Learning](https://github.com/PRML/PRML) - This package contains the matlab implementation of the algorithms described in the book Pattern Recognition and Machine Learning by C. Bishop.
* [OpenCVDotNet](https://code.google.com/p/opencvdotnet/) - A wrapper for the OpenCV project to be used with .NET applications.
* [Emgu CV](http://www.emgu.com/wiki/index.php/Main_Page) - Cross platform wrapper of OpenCV which can be compiled in Mono to e run on Windows, Linus, Mac OS X, iOS, and Android.
* [Stanford.NLP for .NET](https://github.com/sergey-tihon/Stanford.NLP.NET/) - A full port of Stanford NLP packages to .NET and also available precompiled as a NuGet package.
* [Accord.MachineLearning](http://www.nuget.org/packages/Accord.MachineLearning/) - Support Vector Machines, Decision Trees, Naive Bayesian models, K-means, Gaussian Mixture models and general algorithms such as Ransac, Cross-validation and Grid-Search for machine-learning applications. This package is part of the Accord.NET Framework.
* [Vulpes](https://github.com/fsprojects/Vulpes) - Deep belief and deep learning implementation written in F# and leverages CUDA GPU execution with Alea.cuBase.
* [Encog](http://www.nuget.org/packages/encog-dotnet-core/) - An advanced neural network and machine learning framework. Encog contains classes to create a wide variety of networks, as well as support classes to normalize and process data for these neural networks. Encog trains using multithreaded resilient propagation. Encog can also make use of a GPU to further speed processing time. A GUI based workbench is also provided to help model and train neural networks.
* [Neural Network Designer](http://bragisoft.com/) - DBMS management system and designer for neural networks. The designer application is developed using WPF, and is a user interface which allows you to design your neural network, query the network, create and configure chat bots that are capable of asking questions and learning from your feed back. The chat bots can even scrape the internet for information to return in their output as well as to use for learning.
* [numl](http://www.nuget.org/packages/numl/) - numl is a machine learning library intended to ease the use of using standard modeling techniques for both prediction and clustering.
* [Math.NET Numerics](http://www.nuget.org/packages/MathNet.Numerics/) - Numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Supports .Net 4.0, .Net 3.5 and Mono on Windows, Linux and Mac; Silverlight 5, WindowsPhone/SL 8, WindowsPhone 8.1 and Windows 8 with PCL Portable Profiles 47 and 344; Android/iOS with Xamarin.
* [Sho](http://research.microsoft.com/en-us/projects/sho/) - Sho is an interactive environment for data analysis and scientific computing that lets you seamlessly connect scripts (in IronPython) with compiled code (in .NET) to enable fast and flexible prototyping. The environment includes powerful and efficient libraries for linear algebra as well as data visualization that can be used from any .NET language, as well as a feature-rich interactive shell for rapid development.
* [MLPNeuralNet](https://github.com/nikolaypavlov/MLPNeuralNet) - Fast multilayer perceptron neural network library for iOS and Mac OS X. MLPNeuralNet predicts new examples by trained neural network. It is built on top of the Apple's Accelerate Framework, using vectorized operations and hardware acceleration if available.
* [SimpleCV](http://simplecv.org/) - An open source computer vision framework that gives access to several high-powered computer vision libraries, such as OpenCV. Written on Python and runs on Mac, Windows, and Ubuntu Linux.
* [NLTK](http://www.nltk.org/) - A leading platform for building Python programs to work with human language data.
* [Pattern](http://www.clips.ua.ac.be/pattern) - A web mining module for the Python programming language. It has tools for natural language processing, machine learning, among others.
* [TextBlob](http://textblob.readthedocs.org/) - Providing a consistent API for diving into common natural language processing (NLP) tasks. Stands on the giant shoulders of NLTK and Pattern, and plays nicely with both.
* [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - Book/iPython notebooks on Probabilistic Programming in Python
* [Featureforge](https://github.com/machinalis/featureforge) A set of tools for creating and testing machine learning features, with a scikit-learn compatible API
* [SimpleAI](http://github.com/simpleai-team/simpleai) Python implementation of many of the artificial intelligence algorithms described on the book "Artificial Intelligence, a Modern Approach". It focuses on providing an easy to use, well documented and tested library.
* [graphlab-create](http://graphlab.com/products/create/docs/) - A library with various machine learning models (regression, clustering, recommender systems, graph analytics, etc.) implemented on top of a disk-backed DataFrame.
* [pyhsmm](https://github.com/mattjj/pyhsmm) - library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.
* [Spearmint](https://github.com/JasperSnoek/spearmint) - Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Larochelle and Ryan P. Adams. Advances in Neural Information Processing Systems, 2012.
* [SciPy](http://www.scipy.org/) - A Python-based ecosystem of open-source software for mathematics, science, and engineering.
* [NumPy](http://www.numpy.org/) - A fundamental package for scientific computing with Python.
* [Numba](http://numba.pydata.org/) - Python JIT (just in time) complier to LLVM aimed at scientific Python by the developers of Cython and NumPy.
* [NetworkX](https://networkx.github.io/) - A high-productivity software for complex networks.
* [Pandas](http://pandas.pydata.org/) - A library providing high-performance, easy-to-use data structures and data analysis tools.
* [Open Mining](https://github.com/avelino/mining) - Business Intelligence (BI) in Python (Pandas web interface)
* [PyMC](https://github.com/pymc-devs/pymc) - Markov Chain Monte Carlo sampling toolkit.
* [zipline](https://github.com/quantopian/zipline) - A Pythonic algorithmic trading library.
* [PyDy](https://pydy.org/) - Short for Python Dynamics, used to assist with workflow in the modeling of dynamic motion based around NumPy, SciPy, IPython, and matplotlib.
* [SymPy](https://github.com/sympy/sympy) - A Python library for symbolic mathematics.
* [statsmodels](https://github.com/statsmodels/statsmodels) - Statistical modeling and econometrics in Python.
* [astropy](http://www.astropy.org/) - A community Python library for Astronomy.
* [matplotlib](http://matplotlib.org/) - A Python 2D plotting library.
* [bokeh](https://github.com/ContinuumIO/bokeh) - Interactive Web Plotting for Python.
* [plotly](https://plot.ly/python) - Collaborative web plotting for Python and matplotlib.
* [vincent](https://github.com/wrobstory/vincent) - A Python to Vega translator.
* [d3py](https://github.com/mikedewar/d3py) - A plottling library for Python, based on [D3.js](http://d3js.org/).
* [ggplot](https://github.com/yhat/ggplot) - Same API as ggplot2 for R.
* [Kartograph.py](https://github.com/kartograph/kartograph.py) - Rendering beautiful SVG maps in Python.
* [pygal](http://pygal.org/) - A Python SVG Charts Creator.
* [Sarah Palin LDA](https://github.com/Wavelets/sarah-palin-lda) - Topic Modeling the Sarah Palin emails.
* [Diffusion Segmentation](https://github.com/Wavelets/diffusion-segmentation) - A collection of image segmentation algorithms based on diffusion methods
* [Scipy Tutorials](https://github.com/Wavelets/scipy-tutorials) - SciPy tutorials. This is outdated, check out scipy-lecture-notes
* [Crab](https://github.com/marcelcaraciolo/crab) - A recommendation engine library for Python
* [group-lasso](https://github.com/fabianp/group_lasso) - Some experiments with the coordinate descent algorithm used in the (Sparse) Group Lasso model
* [jProcessing](https://github.com/kevincobain2000/jProcessing) - Kanji / Hiragana / Katakana to Romaji Converter. Edict Dictionary & parallel sentences Search. Sentence Similarity between two JP Sentences. Sentiment Analysis of Japanese Text. Run Cabocha(ISO--8859-1 configured) in Python.
* [Allen Downey’s Think Complexity Code](https://github.com/AllenDowney/ThinkComplexity) - Code for Allen Downey's book Think Complexity.
* [Allen Downey’s Think OS Code](https://github.com/AllenDowney/ThinkOS) - Text and supporting code for Think OS: A Brief Introduction to Operating Systems.
* [wiki challange](https://github.com/hammer/wikichallenge) - An implementation of Dell Zhang's solution to Wikipedia's Participation Challenge on Kaggle
* [kaggle insults](https://github.com/amueller/kaggle_insults) - Kaggle Submission for "Detecting Insults in Social Commentary"
* [Treat](https://github.com/louismullie/treat) - Text REtrieval and Annotation Toolkit, definitely the most comprehensive toolkit I’ve encountered so far for Ruby
* [Ruby Linguistics](http://www.deveiate.org/projects/Linguistics/) - Linguistics is a framework for building linguistic utilities for Ruby objects in any language. It includes a generic language-independant front end, a module for mapping language codes into language names, and a module which contains various English-language utilities.
* [UEA Stemmer](https://github.com/ealdent/uea-stemmer) - Ruby port of UEALite Stemmer - a conservative stemmer for search and indexing
* [Twitter-text-rb](https://github.com/twitter/twitter-text-rb) - A library that does auto linking and extraction of usernames, lists and hashtags in tweets
* [Neural Networks and Deep Learning](https://github.com/mnielsen/neural-networks-and-deep-learning) - Code samples for my book "Neural Networks and Deep Learning" [DEEP LEARNING]
* [rsruby](https://github.com/alexgutteridge/rsruby) - Ruby - R bridge
* [data-visualization-ruby](https://github.com/chrislo/data_visualisation_ruby) - Source code and supporting content for my Ruby Manor presentation on Data Visualisation with Ruby
* [ruby-plot](https://www.ruby-toolbox.com/projects/ruby-plot) - gnuplot wrapper for ruby, especially for plotting roc curves into svg files
* [plot-rb](https://github.com/zuhao/plotrb) - A plotting library in Ruby built on top of Vega and D3.
* [Listof](https://github.com/kevincobain2000/listof) - Community based data collection, packed in gem. Get list of pretty much anything (stop words, countries, non words) in txt, json or hash. [Demo/Search for a list](http://listof.herokuapp.com/)
* [h2o](http://cran.r-project.org/web/packages/h2o/index.html) - A framework for fast, parallel, and distributed machine learning algorithms at scale -- Deeplearning, Random forests, GBM, KMeans, PCA, GLM
* [rpart](http://cran.r-project.org/web/packages/rpart/index.html) - rpart: Recursive Partitioning and Regression Trees
* [randomForest](http://cran.r-project.org/web/packages/randomForest/index.html) - randomForest: Breiman and Cutler's random forests for classification and
* [elasticnet](http://cran.r-project.org/web/packages/elasticnet/index.html) - elasticnet: Elastic-Net for Sparse Estimation and Sparse PCA
* [ElemStatLearn](http://cran.r-project.org/web/packages/ElemStatLearn/index.html) - ElemStatLearn: Data sets, functions and examples from the book: "The Elements
of Statistical Learning, Data Mining, Inference, and
Prediction" by Trevor Hastie, Robert Tibshirani and Jerome
Friedman
* [evtree](http://cran.r-project.org/web/packages/evtree/index.html) - evtree: Evolutionary Learning of Globally Optimal Trees
* [frbs](http://cran.r-project.org/web/packages/frbs/index.html) - frbs: Fuzzy Rule-based Systems for Classification and Regression Tasks
* [GAMBoost](http://cran.r-project.org/web/packages/GAMBoost/index.html) - GAMBoost: Generalized linear and additive models by likelihood based
boosting
* [gamboostLSS](http://cran.r-project.org/web/packages/gamboostLSS/index.html) - gamboostLSS: Boosting Methods for GAMLSS
* [glmnet](http://cran.r-project.org/web/packages/glmnet/index.html) - glmnet: Lasso and elastic-net regularized generalized linear models
* [glmpath](http://cran.r-project.org/web/packages/glmpath/index.html) - glmpath: L1 Regularization Path for Generalized Linear Models and Cox
Proportional Hazards Model
* [GMMBoost](http://cran.r-project.org/web/packages/GMMBoost/index.html) - GMMBoost: Likelihood-based Boosting for Generalized mixed models
* [grplasso](http://cran.r-project.org/web/packages/grplasso/index.html) - grplasso: Fitting user specified models with Group Lasso penalty
* [grpreg](http://cran.r-project.org/web/packages/grpreg/index.html) - grpreg: Regularization paths for regression models with grouped
* [oblique.tree](http://cran.r-project.org/web/packages/oblique.tree/index.html) - oblique.tree: Oblique Trees for Classification Data
* [pamr](http://cran.r-project.org/web/packages/pamr/index.html) - pamr: Pam: prediction analysis for microarrays
* [party](http://cran.r-project.org/web/packages/party/index.html) - party: A Laboratory for Recursive Partytioning
* [partykit](http://cran.r-project.org/web/packages/partykit/index.html) - partykit: A Toolkit for Recursive Partytioning
* [penalized](http://cran.r-project.org/web/packages/penalized/index.html) - penalized: L1 (lasso and fused lasso) and L2 (ridge) penalized estimation
in GLMs and in the Cox model
* [penalizedLDA](http://cran.r-project.org/web/packages/penalizedLDA/index.html) - penalizedLDA: Penalized classification using Fisher's linear discriminant
* [penalizedSVM](http://cran.r-project.org/web/packages/penalizedSVM/index.html) - penalizedSVM: Feature Selection SVM using penalty functions
* [randomForest](http://cran.r-project.org/web/packages/randomForest/index.html) - randomForest: Breiman and Cutler's random forests for classification and
regression
* [randomForestSRC](http://cran.r-project.org/web/packages/randomForestSRC/index.html) - randomForestSRC: Random Forests for Survival, Regression and Classification
(RF-SRC)
* [rattle](http://cran.r-project.org/web/packages/rattle/index.html) - rattle: Graphical user interface for data mining in R
* [rgenoud](http://cran.r-project.org/web/packages/rgenoud/index.html) - rgenoud: R version of GENetic Optimization Using Derivatives
* [rgp](http://cran.r-project.org/web/packages/rgp/index.html) - rgp: R genetic programming framework
* [Rmalschains](http://cran.r-project.org/web/packages/Rmalschains/index.html) - Rmalschains: Continuous Optimization using Memetic Algorithms with Local
Search Chains (MA-LS-Chains) in R
* [rminer](http://cran.r-project.org/web/packages/rminer/index.html) - rminer: Simpler use of data mining methods (e.g. NN and SVM) in
classification and regression
* [ROCR](http://cran.r-project.org/web/packages/ROCR/index.html) - ROCR: Visualizing the performance of scoring classifiers
* [RoughSets](http://cran.r-project.org/web/packages/RoughSets/index.html) - RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories
* [rpart](http://cran.r-project.org/web/packages/rpart/index.html) - rpart: Recursive Partitioning and Regression Trees
* [RPMM](http://cran.r-project.org/web/packages/RPMM/index.html) - RPMM: Recursively Partitioned Mixture Model
* [RSNNS](http://cran.r-project.org/web/packages/RSNNS/index.html) - RSNNS: Neural Networks in R using the Stuttgart Neural Network
* [ScalaNLP](http://www.scalanlp.org/) - ScalaNLP is a suite of machine learning and numerical computing libraries.
* [Breeze](https://github.com/scalanlp/breeze) - Breeze is a numerical processing library for Scala.
* [Chalk](https://github.com/scalanlp/chalk) - Chalk is a natural language processing library.
* [FACTORIE](https://github.com/factorie/factorie) - FACTORIE is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, estimating parameters and performing inference.
* [adam](https://github.com/bigdatagenomics/adam) - A genomics processing engine and specialized file format built using Apache Avro, Apache Spark and Parquet. Apache 2 licensed.
