tag:joss.theoj.org,2005:/papers/tagged/gaussian%20processJournal of Open Source Software2024-03-13T22:02:50ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/43602024-03-13T22:02:50Z2024-03-20T22:49:13ZGPCERF - An R package for implementing Gaussian processes for estimating causal exposure response curvesacceptedv0.2.12023-03-23 15:46:21 UTC952024-03-13 22:02:50 UTC920245465NaeemKhoshnevisUniversity Research Computing and Data Services, Harvard University, Cambridge, Massachusetts, United States of America0000-0003-4315-1426BoyuRenMcLean Hospital, Belmont, Massachusetts, United States of America0000-0002-5300-1184DanielleBraunDepartment of Biostatistics, Harvard School of Public Health, Cambridge, Massachusetts, United States of America0000-0002-5177-859810.21105/joss.05465https://doi.org/10.5281/zenodo.10757333R, C++https://joss.theoj.org/papers/10.21105/joss.05465.pdfcausal inference, Gaussian Processes, causal exposure response functiontag:joss.theoj.org,2005:Paper/45382024-02-29T10:31:27Z2024-03-01T17:39:01ZPyBADS: Fast and robust black-box optimization in Pythonacceptedv1.0.02023-06-11 13:10:30 UTC942024-02-29 10:31:27 UTC920245694GurjeetSangraSinghUniversity of Geneva, University of Applied Sciences and Arts Western Switzerland (HES-SO)0009-0008-2340-5867LuigiAcerbiUniversity of Helsinki0000-0001-7471-733610.21105/joss.05694https://doi.org/10.5281/zenodo.10696782Pythonhttps://joss.theoj.org/papers/10.21105/joss.05694.pdfBayesian optimization, Black-box optimization, Optimization, Machine learning, Gaussian Processestag:joss.theoj.org,2005:Paper/45742023-12-18T18:08:54Z2023-12-19T00:00:30Zparafields: A generator for distributed, stationary Gaussian processesacceptedv0.3.02023-07-03 12:18:52 UTC922023-12-18 18:08:54 UTC820235735DominicKempfScientific Software Center, Heidelberg University, Heidelberg, Germany, Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany0000-0002-6140-2332OleKleinIndependent Researcher, Heidelberg, Germany0000-0002-3295-7347RobertKutriInterdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany, Institute for Mathematics, Heidelberg University, Heidelberg, Germany0009-0004-8123-4673RobertScheichlInterdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany, Institute for Mathematics, Heidelberg University, Heidelberg, Germany0000-0001-8493-4393PeterBastianInterdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany10.21105/joss.05735https://doi.org/10.5281/zenodo.10355636Python, C++, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.05735.pdfMPI, scientific computing, high performance computing, uncertainty quantification, random field generation, circulant embeddingtag:joss.theoj.org,2005:Paper/39932023-07-07T21:15:12Z2023-08-23T05:55:41ZSigCorr: A Python package for studies of trials factorsaccepted4.0.02022-11-09 16:10:15 UTC872023-07-07 21:15:12 UTC820234989V.AnanievDepartment of Physics, University of Oslo, Boks 1072 Blindern, Oslo, NO-0316, Norway0000-0003-3649-7621A.L.ReadDepartment of Physics, University of Oslo, Boks 1072 Blindern, Oslo, NO-0316, Norway0000-0002-5751-663610.21105/joss.04989https://doi.org/10.5281/zenodo.8096892Jupyter Notebook, Pythonhttps://joss.theoj.org/papers/10.21105/joss.04989.pdfpython, statistics, look-elsewhere effect, lee, trials factor, statistical significance, gaussian process, resonance searchtag:joss.theoj.org,2005:Paper/34202022-12-08T21:58:29Z2022-12-09T03:31:51ZPyNM: a Lightweight Python implementation of Normative Modelingaccepted1.0.0b12022-03-11 18:20:42 UTC802022-12-08 21:58:29 UTC720224321AnnabelleHarveyCentre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Université de Montréal, QC, Canada, Centre de Recherche du CHU Sainte-Justine, Université de Montréal, QC, Canada0000-0002-9940-8799GuillaumeDumasCentre de Recherche du CHU Sainte-Justine, Université de Montréal, QC, Canada, Mila - Quebec AI Institute, Université de Montréal, QC, Canada0000-0002-2253-184410.21105/joss.04321https://doi.org/10.5281/zenodo.7396721Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.04321.pdfNormative Modeling, Heterogeneity, Heteroskedasticity, Big Data, Centiles, LOESS, Gaussian Process, Stochastic Variational Gaussian Process, GAMLSS, Computational Psychiatry, Neurosciencetag:joss.theoj.org,2005:Paper/35082022-10-09T08:04:39Z2022-10-10T00:01:35Zegobox, a Rust toolbox for efficient global optimizationaccepted0.2.12022-04-20 07:35:38 UTC782022-10-09 08:04:39 UTC720224737RémiLafageONERA, Université de Toulouse, France0000-0001-5479-296110.21105/joss.04737https://doi.org/10.5281/zenodo.7159007Rust, Pythonhttps://joss.theoj.org/papers/10.21105/joss.04737.pdfdesign of experiments, gaussian process, mixture of experts, surrogate-based optimizationtag:joss.theoj.org,2005:Paper/36252022-07-26T20:35:08Z2022-07-27T00:00:31ZGPJax: A Gaussian Process Framework in JAXacceptedv0.4.52022-05-24 16:24:45 UTC752022-07-26 20:35:08 UTC720224455ThomasPinderDepartment of Mathematics and Statistics, Lancaster University, United KingdomDanielDoddSTOR-i Centre for Doctoral Training, Lancaster University, United Kingdom10.21105/joss.04455https://doi.org/10.5281/zenodo.6882220Pythonhttps://joss.theoj.org/papers/10.21105/joss.04455.pdfGaussian processes, JAX, Bayesian inference, Machine learning, Statisticstag:joss.theoj.org,2005:Paper/33182022-04-12T18:05:07Z2022-04-13T00:01:12Ztesliper: a theoretical spectroscopist's little helperaccepted0.9.02022-02-02 15:18:26 UTC722022-04-12 18:05:07 UTC720224164MichałM.WięcławInstitute of Organic Chemistry, Polish Academy of Sciences0000-0001-7884-898210.21105/joss.04164https://doi.org/10.5281/zenodo.6448370Pythonhttps://joss.theoj.org/papers/10.21105/joss.04164.pdfchemistry, spectroscopy, Gaussian, chemical computing, optical spectroscopy, spectral simulations, workflow automation, batch processingtag:joss.theoj.org,2005:Paper/30082022-03-15T11:02:17Z2022-03-15T11:02:21ZRandomForestsGLS: An R package for Random Forests for dependent dataacceptedv0.1.22021-09-02 14:50:53 UTC712022-03-15 11:02:17 UTC720223780ArkajyotiSahaDepartments of Statistics, University of WashingtonSumantaBasuDepartment of Statistics and Data Science, Cornell UniversityAbhirupDattaDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health10.21105/joss.03780https://doi.org/10.5281/zenodo.6257157R, C++, Chttps://joss.theoj.org/papers/10.21105/joss.03780.pdfspatial statistics, Gaussian Processes, Random forests, generalized least-squarestag:joss.theoj.org,2005:Paper/23792021-07-12T11:11:58Z2021-07-13T00:03:33Zstarry_process: Interpretable Gaussian processes for stellar light curvesacceptedv0.9.62021-02-09 15:32:47 UTC632021-07-12 11:11:58 UTC620213071RodrigoLugerCenter for Computational Astrophysics, Flatiron Institute, New York, NY, Virtual Planetary Laboratory, University of Washington, Seattle, WA0000-0002-0296-3826DanielForeman-MackeyCenter for Computational Astrophysics, Flatiron Institute, New York, NY0000-0002-9328-5652ChristinaHedgesBay Area Environmental Research Institute, P.O. Box 25, Moffett Field, CA 94035, USA, NASA Ames Research Center, Moffett Field, CA0000-0002-3385-839110.21105/joss.03071https://doi.org/10.5281/zenodo.4665400Python, C++, Chttps://joss.theoj.org/papers/10.21105/joss.03071.pdfastronomy