Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Mark W. Donoghoe, Ian C. Marschner
Title: logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model
Abstract: Relative risk regression using a log-link binomial generalized linear model (GLM) is an important tool for the analysis of binary outcomes. However, Fisher scoring, which is the standard method for fitting GLMs in statistical software, may have difficulties in converging to the maximum likelihood estimate due to implicit parameter constraints. logbin is an R package that implements several algorithms for fitting relative risk regression models, allowing stable maximum likelihood estimation while ensuring the required parameter constraints are obeyed. We describe the logbin package and examine its stability and speed for different computational algorithms. We also describe how the package may be used to include flexible semi-parametric terms in relative risk regression models.

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Paper: logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model     Download PDF (Downloads: 8346)
logbin_2.0.4.tar.gz: R source package Download (Downloads: 144; 39KB) Replication materials Download (Downloads: 123; 14KB)

DOI: 10.18637/jss.v086.i09

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.