tag:joss.theoj.org,2005:/papers/by/Nima%20HejaziJournal of Open Source Software2022-09-23T14:45:34ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/36132022-09-23T14:45:34Z2022-09-25T16:58:01Zhaldensify: Highly adaptive lasso conditional density estimation in Racceptedv0.2.52022-05-18 15:31:04 UTC772022-09-23 14:45:34 UTC720224522NimaS.HejaziDepartment of Biostatistics, T.H. Chan School of Public Health, Harvard University0000-0002-7127-2789MarkJ.van der LaanDivision of Biostatistics, School of Public Health, University of California, Berkeley, Department of Statistics, University of California, Berkeley0000-0002-1019-8343DavidBenkeserDepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University0000-0002-1019-834310.21105/joss.04522https://doi.org/10.5281/zenodo.7089147Rhttps://joss.theoj.org/papers/10.21105/joss.04522.pdfmachine learning, causal inference, conditional density estimation, generalized propensity score, inverse probability weighting, semiparametric inferencetag:joss.theoj.org,2005:Paper/31652022-01-05T20:07:56Z2023-10-14T09:29:53Zmedoutcon: Nonparametric efficient causal mediation analysis with machine learning in Racceptedv0.1.52021-11-10 22:10:23 UTC692022-01-05 20:07:56 UTC720223979NimaS.HejaziDivision of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, USA0000-0002-7127-2789KaraE.RudolphDepartment of Epidemiology, Mailman School of Public Health, Columbia University, USA0000-0002-9417-7960IvánDíazDivision of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, USA0000-0001-9056-204710.21105/joss.03979https://doi.org/10.5281/zenodo.5809520Rhttps://joss.theoj.org/papers/10.21105/joss.03979.pdfcausal inference, machine learning, semiparametric estimation, mediation analysis, natural direct effect, interventional direct effecttag:joss.theoj.org,2005:Paper/26072021-07-26T17:03:18Z2023-10-09T15:38:04ZcvCovEst: Cross-validated covariance matrix estimator selection and evaluation in Raccepted3.5.02021-04-29 23:42:09 UTC632021-07-26 17:03:18 UTC620213273PhilippeBoileauGraduate Group in Biostatistics, University of California, Berkeley, Center for Computational Biology, University of California, Berkeley0000-0002-4850-2507NimaS.HejaziGraduate Group in Biostatistics, University of California, Berkeley, Center for Computational Biology, University of California, Berkeley0000-0002-7127-2789BrianCollicaDepartment of Statistics, University of California, Berkeley0000-0003-1127-2557MarkJ.van der LaanCenter for Computational Biology, University of California, Berkeley, Department of Statistics, University of California, Berkeley, Division of Biostatistics, School of Public Health, University of California, Berkeley0000-0003-1432-5511SandrineDudoitCenter for Computational Biology, University of California, Berkeley, Department of Statistics, University of California, Berkeley, Division of Biostatistics, School of Public Health, University of California, Berkeley0000-0002-6069-862910.21105/joss.03273https://doi.org/10.5281/zenodo.5132903Rhttps://joss.theoj.org/papers/10.21105/joss.03273.pdfcovariance matrix, cross-validation, high-dimensional statistics, loss-based estimation, multivariate analysistag:joss.theoj.org,2005:Paper/16222020-10-07T21:19:35Z2021-02-15T11:30:45Ztxshift: Efficient estimation of the causal effects of stochastic interventions in Raccepted0.3.32020-05-20 18:51:38 UTC542020-10-07 21:19:35 UTC520202447NimaS.HejaziGraduate Group in Biostatistics, University of California, Berkeley, Center for Computational Biology, University of California, Berkeley0000-0002-7127-2789DavidBenkeserDepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University0000-0002-1019-834310.21105/joss.02447https://doi.org/10.5281/zenodo.4070043Rhttps://joss.theoj.org/papers/10.21105/joss.02447.pdfcausal inference, machine learning, two-phase sampling, efficient estimation, targeted learning, stochastic intervention, modified treatment policytag:joss.theoj.org,2005:Paper/17942020-09-26T07:36:15Z2021-02-15T11:30:26Zhal9001: Scalable highly adaptive lasso regression in Racceptedv0.2.62020-06-24 02:07:17 UTC532020-09-26 07:36:15 UTC520202526NimaS.HejaziGraduate Group in Biostatistics, University of California, Berkeley, Division of Biostatistics, School of Public Health, University of California, Berkeley, Center for Computational Biology, University of California, Berkeley0000-0002-7127-2789JeremyR.CoyleDivision of Biostatistics, School of Public Health, University of California, Berkeley0000-0002-9874-6649MarkJ.van der LaanDivision of Biostatistics, School of Public Health, University of California, Berkeley, Department of Statistics, University of California, Berkeley, Center for Computational Biology, University of California, Berkeley0000-0003-1432-551110.21105/joss.02526https://doi.org/10.5281/zenodo.4050561R, C++https://joss.theoj.org/papers/10.21105/joss.02526.pdfmachine learning, targeted learning, causal inferencetag:joss.theoj.org,2005:Paper/14572020-02-25T00:42:46Z2021-02-15T11:31:05ZscPCA: A toolbox for sparse contrastive principal component analysis in Racceptedv1.1.62020-01-28 17:27:57 UTC462020-02-25 00:42:46 UTC520202079PhilippeBoileauGraduate Group in Biostatistics, University of California, Berkeley0000-0002-4850-2507NimaS.HejaziGraduate Group in Biostatistics, University of California, Berkeley, Center for Computational Biology, University of California, Berkeley0000-0002-7127-2789SandrineDudoitCenter for Computational Biology, University of California, Berkeley, Department of Statistics, University of California, Berkeley, Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley0000-0002-6069-862910.21105/joss.02079https://doi.org/10.5281/zenodo.3686201Rhttps://joss.theoj.org/papers/10.21105/joss.02079.pdfdimensionality reduction, principal component analysis, computational biology, unwanted variation, sparsitytag:joss.theoj.org,2005:Paper/1202018-10-19T00:30:17Z2021-02-15T11:34:21Zadaptest: Data-Adaptive Statistics for High-Dimensional Testing in Raccepted0.2.02016-11-22 00:17:26 UTC302018-10-19 00:30:17 UTC32018161WeixinCaiGroup in Biostatistics, University of California, Berkeley0000-0003-2680-3066AlanHubbardGroup in Biostatistics, University of California, Berkeley0000-0002-3769-0127NimaHejaziGroup in Biostatistics, University of California, Berkeley0000-0002-7127-278910.21105/joss.00161https://doi.org/10.5281/zenodo.1466019Rhttps://joss.theoj.org/papers/10.21105/joss.00161.pdfR language, data-adaptive statistics, data mining, multiple testing, computational biology, bioinformatics, targeted learningtag:joss.theoj.org,2005:Paper/2182018-01-30T11:21:34Z2021-02-15T11:34:04Zorigami: A Generalized Framework for Cross-Validation in Racceptedv0.82017-06-19 21:46:06 UTC212018-01-30 11:21:34 UTC32018512JeremyR.CoyleDivision of Biostatistics, University of California, Berkeley0000-0002-9874-6649NimaS.HejaziDivision of Biostatistics, University of California, Berkeley0000-0002-7127-278910.21105/joss.00512https://doi.org/10.5281/zenodo.1155901Rhttps://joss.theoj.org/papers/10.21105/joss.00512.pdfstatistics, machine learning, cross-validationtag:joss.theoj.org,2005:Paper/1402017-07-26T00:00:00Z2021-02-15T11:34:18Zbiotmle: Targeted Learning for Biomarker Discoveryacceptedv0.1.02017-01-09 01:39:57 UTC152017-07-26 00:00:00 UTC22017295NimaS.HejaziDivision of Biostatistics, University of California, Berkeley0000-0002-7127-2789WeixinCaiDivision of Biostatistics, University of California, Berkeley0000-0003-2680-3066AlanE.HubbardDivision of Biostatistics, University of California, Berkeley0000-0002-3769-012710.21105/joss.00295https://doi.org/10.5281/zenodo.834849Rhttps://joss.theoj.org/papers/10.21105/joss.00295.pdftargeted learning, variable importance, causal inference, bioinformatics, genomics