tag:joss.theoj.org,2005:/papers/tagged/maximum%20likelihoodJournal of Open Source Software2023-02-21T17:40:46ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/33232023-02-21T17:40:46Z2023-02-22T00:00:54ZCVtreeMLE: Efficient Estimation of Mixed Exposures using Data Adaptive Decision Trees and Cross-Validated Targeted Maximum Likelihood Estimation in Racceptedv1.0.02022-02-04 17:57:11 UTC822023-02-21 17:40:46 UTC820234181DavidMcCoyDivision of Environmental Health Sciences, University of California, Berkeley, CA, United States of America0000-0002-5515-6307AlanHubbardDepartment of Biostatistics, University of California, Berkeley, CA, United States of America0000-0002-3769-0127MarkVan der LaanDepartment of Biostatistics, University of California, Berkeley, CA, United States of America0000-0003-1432-551110.21105/joss.04181https://doi.org/10.5281/zenodo.7651354Rhttps://joss.theoj.org/papers/10.21105/joss.04181.pdfcausal inference, machine learning, decision trees, efficient estimation, targeted learning, iterative backfitting, mixed exposurestag:joss.theoj.org,2005:Paper/27562021-08-11T13:49:00Z2022-01-18T12:05:15ZSurPyval: Survival Analysis with Pythonaccepted0.4.02021-06-13 09:16:35 UTC642021-08-11 13:49:00 UTC620213484DerrynKnifeIndependent researcher10.21105/joss.03484https://doi.org/10.5281/zenodo.5177222Pythonhttps://joss.theoj.org/papers/10.21105/joss.03484.pdfsurvival analysis, parameter estimation, censored data, truncated data, maximum likelihood, product spacing estimation, method of moments, mean square error, probability plotting, probability plotting parameter estimationtag:joss.theoj.org,2005:Paper/13172019-12-04T15:38:58Z2021-02-15T11:31:25ZunivariateML: An R package for maximum likelihood estimation of univariate densitiesacceptedv1.0.02019-11-01 20:49:26 UTC442019-12-04 15:38:58 UTC420191863JonasMossUniversity of Oslo0000-0002-6876-696410.21105/joss.01863https://doi.org/10.5281/zenodo.3562385Rhttps://joss.theoj.org/papers/10.21105/joss.01863.pdfstatistics, maximum likelihood, density estimationtag:joss.theoj.org,2005:Paper/9162019-08-04T17:23:38Z2021-02-15T11:32:27Zlifelines: survival analysis in Pythonaccepted0.20.02019-03-06 19:24:40 UTC402019-08-04 17:23:38 UTC420191317CameronDavidson-PilonIndependent researcher0000-0003-1794-914310.21105/joss.01317https://doi.org/10.5281/zenodo.805993Pythonhttps://joss.theoj.org/papers/10.21105/joss.01317.pdfsurvival analysis, reliability analysis, maximum likelihoodtag:joss.theoj.org,2005:Paper/7152018-11-29T14:21:51Z2021-02-15T11:32:50Zssdtools: An R package to fit Species Sensitivity Distributionsacceptedv0.0.32018-11-01 17:39:02 UTC312018-11-29 14:21:51 UTC320181082JoeThorleyPoisson Consulting Ltd., Nelson, Canada0000-0002-7683-4592CarlSchwarzSimon Fraser University, Vancouver, Canada10.21105/joss.01082https://doi.org/10.5281/zenodo.1651641Rhttps://joss.theoj.org/papers/10.21105/joss.01082.pdfssd, maximum likelihood, hazard concentration