OBsMD-package: Objective Bayesian Model Discrimination in Follow-Up Designs

In OBsMD: Objective Bayesian Model Discrimination in Follow-Up Designs

Objective Bayesian Model Discrimination in Follow-Up Designs

Description

Implements the objective Bayesian methodology proposed in Consonni and Deldossi in order to choose the optimal experiment that better discriminate between competing models.

Details

Package:	OBsMD
Type:	Package
Version:	11.1
Date:	2023-11-14
License:	GPL version 3 or later

The packages allows you to perform the calculations and analyses described in Consonni and Deldossi paper in TEST (2016), Objective Bayesian model discrimination in follow-up experimental designs.

Author(s)

Author: Laura Deldossi and Marta Nai Ruscone based on Daniel Meyer's code.\ Maintainer: Marta Nai Ruscone <[e-mail]>

References

Deldossi, L., Nai Ruscone, M. (2020) R Package OBsMD for Follow-up Designs in an Objective Bayesian Framework. Journal of Statistical Software 94(2), 1–37. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v094.i02")}.

Consonni, G. and Deldossi, L. (2016) Objective Bayesian Model Discrimination in Follow-up design., Test 25(3), 397–412. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11749-015-0461-3")}.

Box, G. E. P. and Meyer R. D. (1986) An Analysis of Unreplicated Fractional Factorials., Technometrics 28(1), 11–18. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00401706.1986.10488093")}.

Box, G. E. P. and Meyer R. D. (1993) Finding the Active Factors in Fractionated Screening Experiments., Journal of Quality Technology 25(2), 94–105. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00224065.1993.11979432")}.

Meyer, R. D., Steinberg, D. M. and Box, G. E. P. (1996) Follow-Up Designs to Resolve Confounding in Multifactor Experiments (with discussion)., Technometrics 38(4), 303–332. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2307/1271297")}.

Examples

    data(BM86.data)

OBsMD documentation built on Nov. 14, 2023, 5:10 p.m.