Implements the objective Bayesian methodology proposed in Consonni and Deldossi in order to choose the optimal experiment that better discriminate between competing models.
Author: Laura Deldossi and Marta Nai Ruscone based on Daniel Meyer's code.\ Maintainer: Marta Nai Ruscone <marta.nairuscone@unige.it>
| Package: | OBsMD |
| Type: | Package |
| Version: | 12.0 |
| Date: | 2024-08-19 |
| 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.
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. 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. 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. 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. 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. tools:::Rd_expr_doi("10.2307/1271297").