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
Authors: Laura Deldossi, Marta Nai Ruscone
Title: R Package OBsMD for Follow-Up Designs in an Objective Bayesian Framework
Abstract: Fractional factorial experiments often produce ambiguous results due to confounding among the factors; as a consequence more than one model is consistent with the data. Thus, the practical problem is how to choose additional runs in order to discriminate among the rival models and to identify the active factors. The R package OBsMD solves this problem by implementing the objective Bayesian methodology proposed by Consonni and Deldossi (2016). The main feature of this approach is that the follow-up designs are obtained through the use of just two functions, OBsProb() and OMD() without requiring any prior specifications, being fully automatic. Thus OBsMD provides a simple tool for conducting a design of experiments to solve real world problems.

Page views:: 2396. Submitted: 2017-04-20. Published: 2020-06-30.
Paper: R Package OBsMD for Follow-Up Designs in an Objective Bayesian Framework     Download PDF (Downloads: 681)
OBsMD_6.1.tar.gz: R source package Download (Downloads: 44; 100KB)
v94i02.R: R replication code Download (Downloads: 62; 17KB)

DOI: 10.18637/jss.v094.i02

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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.