tag:joss.theoj.org,2005:/papers/tagged/outlier%20detectionJournal of Open Source Software2023-02-13T13:30:07ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/38312023-02-13T13:30:07Z2023-02-14T00:00:51ZenetLTS: Robust and Sparse Methods for High Dimensional Linear, Binary, and Multinomial Regressionaccepted1.1.02022-08-23 09:31:03 UTC822023-02-13 13:30:07 UTC820234773FatmaSevincKurnazDepartment of Statistics, Yildiz Technical University, Istanbul, Turkey0000-0002-5958-7366PeterFilzmoserInstitute of Statistics and Mathematical Methods in Economics, TU Wien, Vienna, Austria0000-0002-8014-468210.21105/joss.04773https://doi.org/10.5281/zenodo.7598948Rhttps://joss.theoj.org/papers/10.21105/joss.04773.pdfRobust regression, Elastic net, outlier detectiontag:joss.theoj.org,2005:Paper/25882021-06-06T09:39:04Z2021-06-07T00:01:23Zwbacon: Weighted BACON algorithms for multivariate outlier nomination (detection) and robust linear regressionacceptedv0.32021-04-22 10:04:52 UTC622021-06-06 09:39:04 UTC620213238TobiasSchochUniversity of Applied Sciences and Arts Northwestern Switzerland, School of Business, Riggenbachstrasse 16, CH-4600 Olten, Switzerland0000-0002-1640-339510.21105/joss.03238https://doi.org/10.5281/zenodo.4895167R, Chttps://joss.theoj.org/papers/10.21105/joss.03238.pdfoutlier detection, robustness, survey, linear regression, bounded influencetag:joss.theoj.org,2005:Paper/22122021-01-05T11:31:37Z2021-02-15T11:29:41ZLinRegOutliers: A Julia package for detecting outliers in linear regressionacceptedv0.8.32020-12-02 18:04:24 UTC572021-01-05 11:31:37 UTC620212892MehmetHakanSatmanDepartment of Econometrics, Istanbul University, Istanbul, Turkey0000-0002-9402-1982ShreeshAdigaDepartment of Electronics and Communication Engineering, RV College of Engineering, Bengaluru, India0000-0002-1818-6961GuillermoAngerisDepartment of Electrical Engineering, Stanford University, Stanford, California, USA0000-0002-4950-3990EmreAkadalDepartment of Informatics, Istanbul University, Istanbul, Turkey0000-0001-6817-012710.21105/joss.02892https://doi.org/10.5281/zenodo.4419418Juliahttps://joss.theoj.org/papers/10.21105/joss.02892.pdflinear regression, outlier detection, robust statisticstag:joss.theoj.org,2005:Paper/11642019-10-10T14:52:47Z2021-02-15T11:31:46Zmolic: An R package for multivariate outlier detection in contingency tablesacceptedv0.5.02019-08-16 06:45:54 UTC422019-10-10 14:52:47 UTC420191665MadsLindskouDepartment of Mathematical Sciences, Aalborg University, Denmark, Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark0000-0002-1033-697X10.21105/joss.01665https://doi.org/10.5281/zenodo.3475854R, C++https://joss.theoj.org/papers/10.21105/joss.01665.pdfRcpp, outlier detection, contingency tables, graphical models, decomposable graphstag:joss.theoj.org,2005:Paper/9232019-07-02T16:58:58Z2021-02-15T11:32:25ZRAFF.jl: Robust Algebraic Fitting Function in Juliaacceptedv0.4.12019-03-12 19:01:46 UTC392019-07-02 16:58:58 UTC420191385EmersonV.CastelaniDepartment of Mathematics, State University of Maringá, Paraná, Brazil0000-0001-9718-6486RonaldoLopesDepartment of Mathematics, State University of Maringá, Paraná, BrazilWesleyV.ShirabayashiDepartment of Mathematics, State University of Maringá, Paraná, Brazil0000-0002-7790-6703FranciscoN. c.SobralDepartment of Mathematics, State University of Maringá, Paraná, Brazil0000-0003-4963-094610.21105/joss.01385https://doi.org/10.5281/zenodo.3265300Juliahttps://joss.theoj.org/papers/10.21105/joss.01385.pdfStatistics, Lower Order-Value Optimization, Outlier detection, Nonlinear optimizationtag:joss.theoj.org,2005:Paper/9122019-03-29T11:09:29Z2021-02-15T11:32:27Zrrcf: Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streamsacceptedv0.22019-03-04 21:56:28 UTC352019-03-29 11:09:29 UTC420191336MatthewD.BartosDepartment of Civil and Environmental Engineering, University of Michigan0000-0001-6421-222XAbhiramMullapudiDepartment of Civil and Environmental Engineering, University of Michigan0000-0001-8141-3621SaraC.TroutmanDepartment of Civil and Environmental Engineering, University of Michigan0000-0002-6809-795910.21105/joss.01336https://doi.org/10.5281/zenodo.2613881Pythonhttps://joss.theoj.org/papers/10.21105/joss.01336.pdfoutlier detection, machine learning, ensemble methods, random foreststag:joss.theoj.org,2005:Paper/4922018-10-27T11:05:52Z2021-02-15T11:33:23ZPyNomaly: Anomaly detection using Local Outlier Probabilities (LoOP).acceptedv0.2.02018-05-08 01:02:08 UTC302018-10-27 11:05:52 UTC32018845ValentinoConstantinouNASA Jet Propulsion Laboratory0000-0002-5279-414310.21105/joss.00845https://doi.org/10.5281/zenodo.1472519Pythonhttps://joss.theoj.org/papers/10.21105/joss.00845.pdfoutlier detection, anomaly detection, probability, nearest neighbors, unsupervised learning, machine learning, statistics