tag:joss.theoj.org,2005:/papers/edited_by/@fabian-sJournal of Open Source Software2024-03-09T11:15:15ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/46362024-03-09T11:15:15Z2024-03-10T00:01:20Zkonfound: An R Sensitivity Analysis Package to Quantify the Robustness of Causal Inferencesaccepted0.4.02023-07-31 19:56:25 UTC952024-03-09 11:15:15 UTC920245779SarahNarvaizUniversity of Tennessee, Knoxville, Knoxville, TN, USAQinyunLinUniversity of Gothenburg, Gothenburg, SEJoshuaM.RosenbergUniversity of Tennessee, Knoxville, Knoxville, TN, USAKennethA.FrankMichigan State University, East Lansing, MI, USASpiroJ.MaroulisArizona State University, Tempe, AZ, USAWeiWangUniversity of Tennessee, Knoxville, Knoxville, TN, USARanXuUniversity of Connecticut, Hartford, CT, USA10.21105/joss.05779https://doi.org/10.5281/zenodo.10708094Rhttps://joss.theoj.org/papers/10.21105/joss.05779.pdfSensitivity analysis, Causal inferencetag:joss.theoj.org,2005:Paper/46322023-11-17T14:46:05Z2023-11-28T02:34:05ZDREiMac: Dimensionality Reduction with Eilenberg-MacLane Coordinatesaccepted0.3.02023-07-28 17:34:30 UTC912023-11-17 14:46:05 UTC820235791JoseA.PereaNortheastern University0000-0002-6440-5096LuisScoccolaUniversity of Oxford0000-0002-4862-722XChristopherJ.TralieUrsinus College0000-0003-4206-196310.21105/joss.05791https://doi.org/10.5281/zenodo.10136542Pythonhttps://joss.theoj.org/papers/10.21105/joss.05791.pdftopological data analysis, unsupervised learning, dimensionality reductiontag:joss.theoj.org,2005:Paper/43172023-05-24T18:06:09Z2023-05-25T14:26:02Zsptotal: an R package for predicting totals and weighted sums from spatial dataaccepted1.0.12023-03-17 12:19:46 UTC852023-05-24 18:06:09 UTC820235363MattHighamSt. Lawrence UniversityJayVerHoefNational Oceanic and Atmospheric AdministrationBryceFrankBureau of Land ManagementMichaelDumelleUnited States Environmental Protection Agency0000-0002-3393-552910.21105/joss.05363https://doi.org/10.5281/zenodo.7962631Rhttps://joss.theoj.org/papers/10.21105/joss.05363.pdfkriging, finite population, spatial, predictiontag:joss.theoj.org,2005:Paper/41522023-04-25T14:06:49Z2023-04-26T00:02:35ZTextDescriptives: A Python package for calculating a large variety of metrics from textacceptedv2.1.02023-01-06 10:36:03 UTC842023-04-25 14:06:49 UTC820235153LasseHansenDepartment of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark, Center for Humanities Computing, Aarhus University, Aarhus, Denmark0000-0003-1113-4779LudvigRenboOlsenDepartment of Molecular Medicine (MOMO), Aarhus University, Aarhus, Denmark, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark0009-0006-6798-7454KennethEnevoldsenDepartment of Clinical Medicine, Aarhus University, Aarhus, Denmark, Center for Humanities Computing, Aarhus University, Aarhus, Denmark0000-0001-8733-096610.21105/joss.05153https://doi.org/10.5281/zenodo.7858731Pythonhttps://joss.theoj.org/papers/10.21105/joss.05153.pdfnatural language processing, spacy, feature extractiontag:joss.theoj.org,2005:Paper/42072023-04-11T14:04:20Z2023-04-12T00:02:56ZRegl-Scatterplot: A Scalable Interactive JavaScript-based Scatter Plot Libraryacceptedv1.5.12023-02-15 02:21:06 UTC842023-04-11 14:04:20 UTC820235275FritzLekschasIndependent Researcher, USA0000-0001-8432-483510.21105/joss.05275https://doi.org/10.5281/zenodo.7796642JavaScript, GLSLhttps://joss.theoj.org/papers/10.21105/joss.05275.pdfscatter plot, 2D scatter, interactive data visualization, embedding plot, WebGLtag: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/39282023-02-03T08:46:33Z2023-02-04T00:00:42ZRCaNmodel: An R package for Chance and Necessity modellingacceptedv2.02022-09-27 09:08:34 UTC822023-02-03 08:46:33 UTC820234955HilaireDrouineauINRAE, UR EABX, 50 avenue de Verdun, CEDEX, 33612 Cestas, France0000-0001-9206-0040BenjaminPlanqueInstitute of Marine Research, Norway, P.O. Box 6606, 9296 Tromsø, Norway0000-0002-0557-7410ChristianMullonIRD, UMR MARBEC, Avenue Jean Monnet, Sete, France0000-0003-3990-911810.21105/joss.04955https://doi.org/10.5281/zenodo.7595076Java, JavaScript, R, C++https://joss.theoj.org/papers/10.21105/joss.04955.pdftrophic food web model, javafx, trophic controls, linear inverse modellingtag:joss.theoj.org,2005:Paper/35212022-09-29T21:20:38Z2022-09-30T00:00:37ZNiaARM: A minimalistic framework for Numerical Association Rule Miningaccepted0.1.62022-04-27 18:52:09 UTC772022-09-29 21:20:38 UTC720224448ŽigaStupanUniversity of Maribor, Faculty of Electrical Engineering and Computer Science0000-0001-9847-7306IztokFisterUniversity of Maribor, Faculty of Electrical Engineering and Computer Science0000-0002-6418-127210.21105/joss.04448https://doi.org/10.5281/zenodo.7123879Pythonhttps://joss.theoj.org/papers/10.21105/joss.04448.pdfassociation rule mining, data mining, evolutionary algorithms, numerical association rule mining, visualizationtag:joss.theoj.org,2005:Paper/35922022-09-05T11:14:39Z2023-10-08T07:24:41Ztipr: An R package for sensitivity analyses for unmeasured confoundersacceptedv0.4.12022-05-06 16:58:03 UTC772022-09-05 11:14:39 UTC720224495LucyD\'AgostinoMcGowanWake Forest University, USA0000-0001-7297-935910.21105/joss.04495https://doi.org/10.5281/zenodo.6958926Rhttps://joss.theoj.org/papers/10.21105/joss.04495.pdfstatistics, epidemiology, sensitivity analyses, causal inference, confoundingtag:joss.theoj.org,2005:Paper/36062022-06-28T17:26:33Z2022-06-29T00:00:35ZmusclesyneRgies: factorization of electromyographic data in R with sensible defaultsaccepted1.1.32022-05-16 14:23:41 UTC742022-06-28 17:26:33 UTC720224439AlessandroSantuzDepartment of Training and Movement Sciences, Humboldt-Universität zu Berlin, Berlin, Germany, Berlin School of Movement Science, Humboldt-Universität zu Berlin, Berlin, Germany, Institute for Biomechanics, ETH Zurich, Zurich, Switzerland0000-0002-6577-510110.21105/joss.04439https://doi.org/10.5281/zenodo.6767313Rhttps://joss.theoj.org/papers/10.21105/joss.04439.pdfelectromyography, NMF, dimensionality reduction, neurophysiology, biomechanics