2 10 12 https://repository.horizon.ac.id/files/original/839f40ce7ee7ef3db4fc5f210366cd24.pdf 96e2fb4fbbe4019bdd34bf89e9b70eba Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOLUME 106 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v106 Date A point or period of time associated with an event in the lifecycle of the resource 2023 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource netmeta: An R Package for Network Meta-Analysis Using Frequentist Methods Subject The topic of the resource network meta-analysis, R, inconsistency, component network meta-analysis. Description An account of the resource Network meta-analysis compares different interventions for the same condition, by combining direct and indirect evidence derived from all eligible studies. Network metaanalysis has been increasingly used by applied scientists and it is a major research topic for methodologists. This article describes the R package netmeta, which adopts frequentist methods to fit network meta-analysis models. We provide a roadmap to perform network meta-analysis, along with an overview of the main functions of the package. We present three worked examples considering different types of outcomes and different data formats to facilitate researchers aiming to conduct network meta-analysis with netmeta Creator An entity primarily responsible for making the resource Sara Balduzzi Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v106i02 Publisher An entity responsible for making the resource available Universität Freiburg Date A point or period of time associated with an event in the lifecycle of the resource March 2023 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/8cebf5edc7acd7e1673bb366c4cba21f.pdf 13078abd1c3955833a1e220f5c377cb7 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOLUME 106 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v106 Date A point or period of time associated with an event in the lifecycle of the resource 2023 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Elastic Net Regularization Paths for All Generalized Linear Models Subject The topic of the resource : lasso, elastic net, ℓ1 penalty, regularization path, coordinate descent, generalized linear models, survival, Cox mode Description An account of the resource The lasso and elastic net are popular regularized regression models for supervised learning. Friedman, Hastie, and Tibshirani (2010) introduced a computationally efficient algorithm for computing the elastic net regularization path for ordinary least squares regression, logistic regression and multinomial logistic regression, while Simon, Friedman, Hastie, and Tibshirani (2011) extended this work to Cox models for right-censored data. We further extend the reach of the elastic net-regularized regression to all generalized linear model families, Cox models with (start, stop] data and strata, and a simplified version of the relaxed lasso. We also discuss convenient utility functions for measuring the performance of these fitted models Creator An entity primarily responsible for making the resource J. Kenneth Tay Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v106i01 Publisher An entity responsible for making the resource available Stanford University Date A point or period of time associated with an event in the lifecycle of the resource March 2023 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text