Machine Learning Open Source Software

To support the open source software movement, JMLR MLOSS publishes contributions related to implementations of non-trivial machine learning algorithms, toolboxes or even languages for scientific computing. Submission instructions are available here.

solo-learn: A Library of Self-supervised Methods for Visual Representation Learning: Victor Guilherme Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, Elisa Ricci; (56):1−6, 2022.
[abs][pdf][bib] [code]

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization: Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter; (54):1−9, 2022.
[abs][pdf][bib] [code]

DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python: Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler; (53):1−6, 2022.
[abs][pdf][bib] [code]

Toolbox for Multimodal Learn (scikit-multimodallearn): Dominique Benielli, Baptiste Bauvin, Sokol Koço, Riikka Huusari, Cécile Capponi, Hachem Kadri, François Laviolette; (51):1−7, 2022.
[abs][pdf][bib] [code]

Stable-Baselines3: Reliable Reinforcement Learning Implementations: Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, Noah Dormann; (268):1−8, 2021.
[abs][pdf][bib] [code]

DIG: A Turnkey Library for Diving into Graph Deep Learning Research: Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora M Oztekin, Xuan Zhang, Shuiwang Ji; (240):1−9, 2021.
[abs][pdf][bib] [code]

sklvq: Scikit Learning Vector Quantization: Rick van Veen, Michael Biehl, Gert-Jan de Vries; (231):1−6, 2021.
[abs][pdf][bib] [code]

FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection: Yang Liu, Tao Fan, Tianjian Chen, Qian Xu, Qiang Yang; (226):1−6, 2021.
[abs][pdf][bib] [code]

TensorHive: Management of Exclusive GPU Access for Distributed Machine Learning Workloads: Paweł Rościszewski, Michał Martyniak, Filip Schodowski; (215):1−5, 2021.
[abs][pdf][bib] [code]

dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python: Hubert Baniecki, Wojciech Kretowicz, Piotr Piątyszek, Jakub Wiśniewski, Przemysław Biecek; (214):1−7, 2021.
[abs][pdf][bib] [code]

mlr3pipelines - Flexible Machine Learning Pipelines in R: Martin Binder, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, Bernd Bischl; (184):1−7, 2021.
[abs][pdf][bib] [code]

Alibi Explain: Algorithms for Explaining Machine Learning Models: Janis Klaise, Arnaud Van Looveren, Giovanni Vacanti, Alexandru Coca; (181):1−7, 2021.
[abs][pdf][bib] [code]

The ensmallen library for flexible numerical optimization: Ryan R. Curtin, Marcus Edel, Rahul Ganesh Prabhu, Suryoday Basak, Zhihao Lou, Conrad Sanderson; (166):1−6, 2021.
[abs][pdf][bib] [code]

MushroomRL: Simplifying Reinforcement Learning Research: Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters; (131):1−5, 2021.
[abs][pdf][bib] [code]

River: machine learning for streaming data in Python: Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphael Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem, Albert Bifet; (110):1−8, 2021.
[abs][pdf][bib] [code]

mvlearn: Multiview Machine Learning in Python: Ronan Perry, Gavin Mischler, Richard Guo, Theodore Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, Joshua T. Vogelstein; (109):1−7, 2021.
[abs][pdf][bib] [code]

OpenML-Python: an extensible Python API for OpenML: Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter; (100):1−5, 2021.
[abs][pdf][bib] [code]

POT: Python Optimal Transport: Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer; (78):1−8, 2021.
[abs][pdf][bib] [code]

ChainerRL: A Deep Reinforcement Learning Library: Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, Takahiro Ishikawa; (77):1−14, 2021.
[abs][pdf][bib] [code]

Kernel Operations on the GPU, with Autodiff, without Memory Overflows: Benjamin Charlier, Jean Feydy, Joan Alexis Glaunès, François-David Collin, Ghislain Durif; (74):1−6, 2021.
[abs][pdf][bib] [code]

giotto-tda: : A Topological Data Analysis Toolkit for Machine Learning and Data Exploration: Guillaume Tauzin, Umberto Lupo, Lewis Tunstall, Julian Burella Pérez, Matteo Caorsi, Anibal M. Medina-Mardones, Alberto Dassatti, Kathryn Hess; (39):1−6, 2021.
[abs][pdf][bib] [code]

Pykg2vec: A Python Library for Knowledge Graph Embedding: Shih-Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque; (16):1−6, 2021.
[abs][pdf][bib] [code]

algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD: Joseph D. Ramsey, Daniel Malinsky, Kevin V. Bui; (238):1−6, 2020.
[abs][pdf][bib] [code]

Geomstats: A Python Package for Riemannian Geometry in Machine Learning: Nina Miolane, Nicolas Guigui, Alice Le Brigant, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Hadi Zaatiti, Hatem Hajri, Yann Cabanes, Thomas Gerald, Paul Chauchat, Christian Shewmake, Daniel Brooks, Bernhard Kainz, Claire Donnat, Susan Holmes, Xavier Pennec; (223):1−9, 2020.
[abs][pdf][bib] [code]

scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn: Sebastian Pölsterl; (212):1−6, 2020.
[abs][pdf][bib] [code]

Scikit-network: Graph Analysis in Python: Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier; (185):1−6, 2020.
[abs][pdf][bib] [code]

apricot: Submodular selection for data summarization in Python: Jacob Schreiber, Jeffrey Bilmes, William Stafford Noble; (161):1−6, 2020.
[abs][pdf][bib] [code]

metric-learn: Metric Learning Algorithms in Python: William de Vazelhes, CJ Carey, Yuan Tang, Nathalie Vauquier, Aurélien Bellet; (138):1−6, 2020.
[abs][pdf][bib] [code]

Probabilistic Learning on Graphs via Contextual Architectures: Davide Bacciu, Federico Errica, Alessio Micheli; (134):1−39, 2020.
[abs][pdf][bib] [code]

AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models: Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang; (130):1−6, 2020.
[abs][pdf][bib] [code]

Apache Mahout: Machine Learning on Distributed Dataflow Systems: Robin Anil, Gokhan Capan, Isabel Drost-Fromm, Ted Dunning, Ellen Friedman, Trevor Grant, Shannon Quinn, Paritosh Ranjan, Sebastian Schelter, Özgür Yılmazel; (127):1−6, 2020.
[abs][pdf][bib] [code]

Tslearn, A Machine Learning Toolkit for Time Series Data: Romain Tavenard, Johann Faouzi, Gilles Vandewiele, Felix Divo, Guillaume Androz, Chester Holtz, Marie Payne, Roman Yurchak, Marc Rußwurm, Kushal Kolar, Eli Woods; (118):1−6, 2020.
[abs][pdf][bib] [code]

GluonTS: Probabilistic and Neural Time Series Modeling in Python: Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang; (116):1−6, 2020.
[abs][pdf][bib] [code]

MFE: Towards reproducible meta-feature extraction: Edesio Alcobaça, Felipe Siqueira, Adriano Rivolli, Luís P. F. Garcia, Jefferson T. Oliva, André C. P. L. F. de Carvalho; (111):1−5, 2020.
[abs][pdf][bib] [code]

ThunderGBM: Fast GBDTs and Random Forests on GPUs: Zeyi Wen, Hanfeng Liu, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen; (108):1−5, 2020.
[abs][pdf][bib] [code]

AI-Toolbox: A C++ library for Reinforcement Learning and Planning (with Python Bindings): Eugenio Bargiacchi, Diederik M. Roijers, Ann Nowé; (102):1−12, 2020.
[abs][pdf][bib] [code]

pyDML: A Python Library for Distance Metric Learning: Juan Luis Suárez, Salvador García, Francisco Herrera; (96):1−7, 2020.
[abs][pdf][bib] [code]

Cornac: A Comparative Framework for Multimodal Recommender Systems: Aghiles Salah, Quoc-Tuan Truong, Hady W. Lauw; (95):1−5, 2020.
[abs][pdf][bib] [code]

Kymatio: Scattering Transforms in Python: Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim Andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Muawiz Chaudhary, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine Cella, Michael Eickenberg; (60):1−6, 2020.
[abs][pdf][bib] [code]

GraKeL: A Graph Kernel Library in Python: Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, Michalis Vazirgiannis; (54):1−5, 2020.
[abs][pdf][bib] [code]

pyts: A Python Package for Time Series Classification: Johann Faouzi, Hicham Janati; (46):1−6, 2020.
[abs][pdf][bib] [code]

Tensor Train Decomposition on TensorFlow (T3F): Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan Oseledets; (30):1−7, 2020.
[abs][pdf][bib] [code]

ORCA: A Matlab/Octave Toolbox for Ordinal Regression: Javier Sánchez-Monedero, Pedro A. Gutiérrez, María Pérez-Ortiz; (125):1−5, 2019.
[abs][pdf][bib] [code]

PyOD: A Python Toolbox for Scalable Outlier Detection: Yue Zhao, Zain Nasrullah, Zheng Li; (96):1−7, 2019.
[abs][pdf][bib] [code]

iNNvestigate Neural Networks!: Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans; (93):1−8, 2019.
[abs][pdf][bib] [code]

AffectiveTweets: a Weka Package for Analyzing Affect in Tweets: Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer, Saif M. Mohammad; (92):1−6, 2019.
[abs][pdf][bib] [code]

SMART: An Open Source Data Labeling Platform for Supervised Learning: Rob Chew, Michael Wenger, Caroline Kery, Jason Nance, Keith Richards, Emily Hadley, Peter Baumgartner; (82):1−5, 2019.
[abs][pdf][bib] [code]

Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python: Jason Ge, Xingguo Li, Haoming Jiang, Han Liu, Tong Zhang, Mengdi Wang, Tuo Zhao; (44):1−5, 2019.
[abs][pdf][bib] [code] [webpage]

Pyro: Deep Universal Probabilistic Programming: Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, Noah D. Goodman; (28):1−6, 2019.
[abs][pdf][bib] [code]

TensorLy: Tensor Learning in Python: Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic; (26):1−6, 2019.
[abs][pdf][bib] [code]

spark-crowd: A Spark Package for Learning from Crowdsourced Big Data: Enrique G. Rodrigo, Juan A. Aledo, José A. Gámez; (19):1−5, 2019.
[abs][pdf][bib] [code]

scikit-multilearn: A Python library for Multi-Label Classification: Piotr Szymański, Tomasz Kajdanowicz; (6):1−22, 2019.
[abs][pdf][bib] [code]

Seglearn: A Python Package for Learning Sequences and Time Series: David M. Burns, Cari M. Whyne; (83):1−7, 2018.
[abs][pdf][bib] [code] [webpage]

Scikit-Multiflow: A Multi-output Streaming Framework: Jacob Montiel, Jesse Read, Albert Bifet, Talel Abdessalem; (72):1−5, 2018.
[abs][pdf][bib] [code]

OpenEnsembles: A Python Resource for Ensemble Clustering: Tom Ronan, Shawn Anastasio, Zhijie Qi, Pedro Henrique S. Vieira Tavares, Roman Sloutsky, Kristen M. Naegle; (26):1−6, 2018.
[abs][pdf][bib] [webpage] [code]

ThunderSVM: A Fast SVM Library on GPUs and CPUs: Zeyi Wen, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen; (21):1−5, 2018.
[abs][pdf][bib] [webpage] [code]

ELFI: Engine for Likelihood-Free Inference: Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski; (16):1−7, 2018.
[abs][pdf][bib] [webpage] [code]

SGDLibrary: A MATLAB library for stochastic optimization algorithms: Hiroyuki Kasai; (215):1−5, 2018.
[abs][pdf][bib] [code]

tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models: Emmanuel Bacry, Martin Bompaire, Philip Deegan, Stéphane Gaïffas, Søren V. Poulsen; (214):1−5, 2018.
[abs][pdf][bib] [code] [webpage]

KELP: a Kernel-based Learning Platform: Simone Filice, Giuseppe Castellucci, Giovanni Da San Martino, Aless, ro Moschitti, Danilo Croce, Roberto Basili; (191):1−5, 2018.
[abs][pdf][bib] [code] [webpage]

Pycobra: A Python Toolbox for Ensemble Learning and Visualisation: Benjamin Guedj, Bhargav Srinivasa Desikan; (190):1−5, 2018.
[abs][pdf][bib] [code] [webpage]

HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data: Andrew C. Heusser, Kirsten Ziman, Lucy L. W. Owen, Jeremy R. Manning; (152):1−6, 2018.
[abs][pdf][bib] [code] [webpage]

openXBOW -- Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit: Maximilian Schmitt, Björn Schuller; (96):1−5, 2017.
[abs][pdf][bib] [code]

The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems: Frans A. Oliehoek, Matthijs T. J. Spaan, Bas Terwijn, Philipp Robbel, Jo\~{a}o V. Messias; (89):1−5, 2017.
[abs][pdf][bib] [code]

GPflow: A Gaussian Process Library using TensorFlow: Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo Le{\'o}n-Villagr{\'a}, Zoubin Ghahramani, James Hensman; (40):1−6, 2017.
[abs][pdf][bib] [code] [webpage]

GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis: Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski; (39):1−5, 2017.
[abs][pdf][bib] [code] [r-project.org]

POMDPs.jl: A Framework for Sequential Decision Making under Uncertainty: Maxim Egorov, Zachary N. Sunberg, Edward Balaban, Tim A. Wheeler, Jayesh K. Gupta, Mykel J. Kochenderfer; (26):1−5, 2017.
[abs][pdf][bib] [code] [webpage]

Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA: Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown; (25):1−5, 2017.
[abs][pdf][bib] [code] [webpage]

JSAT: Java Statistical Analysis Tool, a Library for Machine Learning: Edward Raff; (23):1−5, 2017.
[abs][pdf][bib] [code] [webpage]

Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning: Guillaume Lemaître, Fernando Nogueira, Christos K. Aridas; (17):1−5, 2017.
[abs][pdf][bib] [code] [webpage]

Refinery: An Open Source Topic Modeling Web Platform: Daeil Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth; (12):1−5, 2017.
[abs][pdf][bib] [code] [webpage]

SnapVX: A Network-Based Convex Optimization Solver: David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosič, Stephen Boyd, Jure Leskovec; (4):1−5, 2017.
[abs][pdf][bib] [code] [stanford.edu]

fastFM: A Library for Factorization Machines: Immanuel Bayer; (184):1−5, 2016.
[abs][pdf][bib] [code] [webpage]

Megaman: Scalable Manifold Learning in Python: James McQueen, Marina Meilă, Jacob VanderPlas, Zhongyue Zhang; (148):1−5, 2016.
[abs][pdf][bib] [code] [webpage]

JCLAL: A Java Framework for Active Learning: Oscar Reyes, Eduardo Pérez, María del Carmen Rodríguez-Hernández, Habib M. Fardoun, Sebastián Ventura; (95):1−5, 2016.
[abs][pdf][bib] [code]

LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems: Wei-Sheng Chin, Bo-Wen Yuan, Meng-Yuan Yang, Yong Zhuang, Yu-Chin Juan, Chih-Jen Lin; (86):1−5, 2016.
[abs][pdf][bib] [code]

CVXPY: A Python-Embedded Modeling Language for Convex Optimization: Steven Diamond, Stephen Boyd; (83):1−5, 2016.
[abs][pdf][bib] [code] [webpage]

Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches: Jure Žbontar, Yann LeCun; (65):1−32, 2016.
[abs][pdf][bib] [code]

MLlib: Machine Learning in Apache Spark: Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar; (34):1−7, 2016.
[abs][pdf][bib] [code] [webpage]

MEKA: A Multi-label/Multi-target Extension to WEKA: Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes; (21):1−5, 2016.
[abs][pdf][bib] [code] [webpage]

Harry: A Tool for Measuring String Similarity: Konrad Rieck, Christian Wressnegger; (9):1−5, 2016.
[abs][pdf][bib] [code] [webpage]

partykit: A Modular Toolkit for Recursive Partytioning in R: Torsten Hothorn, Achim Zeileis; (118):3905−3909, 2015.
[abs][pdf][bib] [code]

CEKA: A Tool for Mining the Wisdom of Crowds: Jing Zhang, Victor S. Sheng, Bryce A. Nicholson, Xindong Wu; (88):2853−2858, 2015.
[abs][pdf][bib] [code]

pyGPs -- A Python Library for Gaussian Process Regression and Classification: Marion Neumann, Shan Huang, Daniel E. Marthaler, Kristian Kersting; (80):2611−2616, 2015.
[abs][pdf][bib] [code]

The Libra Toolkit for Probabilistic Models: Daniel Lowd, Amirmohammad Rooshenas; (75):2459−2463, 2015.
[abs][pdf][bib] [code]

RLPy: A Value-Function-Based Reinforcement Learning Framework for Education and Research: Alborz Geramifard, Christoph Dann, Robert H. Klein, William Dabney, Jonathan P. How; (46):1573−1578, 2015.
[abs][pdf][bib] [code]

Encog: Library of Interchangeable Machine Learning Models for Java and C#: Jeff Heaton; (36):1243−1247, 2015.
[abs][pdf][bib] [code] [webpage]

The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R: Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu; (18):553−557, 2015.
[abs][pdf][bib] [code] [webpage]

Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit: Felix Weninger; (17):547−551, 2015.
[abs][pdf][bib] [code]

A Classification Module for Genetic Programming Algorithms in JCLEC: Alberto Cano, José María Luna, Amelia Zafra, Sebastián Ventura; (15):491−494, 2015.
[abs][pdf][bib] [code]

SAMOA: Scalable Advanced Massive Online Analysis: Gianmarco De Francisci Morales, Albert Bifet; (5):149−153, 2015.
[abs][pdf][bib] [code]

BudgetedSVM: A Toolbox for Scalable SVM Approximations: Nemanja Djuric, Liang Lan, Slobodan Vucetic, Zhuang Wang; (84):3813−3817, 2013.
[abs][pdf][bib] [code]

MULTIBOOST: A Multi-purpose Boosting Package: Djalel Benbouzid, Róbert Busa-Fekete, Norman Casagrande, François-David Collin, Balázs Kégl; (18):549−553, 2012.
[abs][pdf][bib] [code]

Waffles: A Machine Learning Toolkit: Michael Gashler; (69):2383−2387, 2011.
[abs][pdf][bib] [code]

CARP: Software for Fishing Out Good Clustering Algorithms: Volodymyr Melnykov, Ranjan Maitra; (3):69−73, 2011.
[abs][pdf][bib] [code]

LIBLINEAR: A Library for Large Linear Classification: Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin; (61):1871−1874, 2008.
[abs][pdf][bib] [code]