tag:joss.theoj.org,2005:/papers/reviewed_by/@rasbtJournal of Open Source Software2018-10-23T04:23:50ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/5912018-10-23T04:23:50Z2021-02-15T11:33:06Zq2-sample-classifier: machine-learning tools for microbiome classification and regressionaccepted2018.62018-08-10 20:05:52 UTC302018-10-23 04:23:50 UTC32018934NicholasA.BokulichThe Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA0000-0002-1784-8935MatthewR.DillonThe Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA0000-0002-7713-1952EvanBolyenThe Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA0000-0002-5362-6782BenjaminD.KaehlerResearch School of Biology, Australian National University, Canberra, Australia0000-0002-5318-9551GavinA.HuttleyResearch School of Biology, Australian National University, Canberra, Australia0000-0001-7224-2074JGregoryCaporasoThe Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA, Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA0000-0002-8865-167010.21105/joss.00934https://doi.org/10.5281/zenodo.1468878Pythonhttps://joss.theoj.org/papers/10.21105/joss.00934.pdfmicrobiome, supervised learning, amplicon sequencing, metagenomicstag:joss.theoj.org,2005:Paper/4782018-06-18T19:05:30Z2021-02-15T11:33:26Zmlpack 3: a fast, flexible machine learning libraryacceptedv3.0.02018-04-24 20:49:42 UTC262018-06-18 19:05:30 UTC32018726RyanR.CurtinCenter for Advanced Machine Learning, Symantec Corporation0000-0002-9903-8214MarcusEdelInstitute of Computer Science, Free University of Berlin0000-0001-5445-7303MikhailLozhnikovMoscow State University, Faculty of Mechanics and Mathematics0000-0002-8727-0091YannisMentekidisNone0000-0003-3860-9885SumedhGhaisasNone0000-0003-3753-9029ShangtongZhangUniversity of Alberta0000-0003-4255-136410.21105/joss.00726https://doi.org/10.5281/zenodo.1292120C++, Pythonhttps://joss.theoj.org/papers/10.21105/joss.00726.pdfmachine learning, deep learning, c++, optimization, template metaprogrammingtag:joss.theoj.org,2005:Paper/252016-09-07T00:00:00Z2020-03-14T00:52:03ZOsprey: Hyperparameter Optimization for Machine Learningacceptedv1.1.02016-06-29 23:50:05 UTC52016-09-07 00:00:00 UTC1201634RobertT.McGibbonStanford UniversityCarlosX.HernándezStanford University0000-0002-8146-5904MatthewP.HarriganStanford UniversityStevenKearnesStanford UniversityMohammadM.SultanStanford UniversityStanislawJastrzebskiJagiellonian UniversityBrookeE.HusicStanford UniversityVijayS.PandeStanford University10.21105/joss.00034https://doi.org/10.5281/zenodo.61670Pythonhttps://joss.theoj.org/papers/10.21105/joss.00034.pdfoptimization, cross-validation, machine learning