OBsMD (version 12.0)

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, see Deldossi and Nai Ruscone (2020) .

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Version

Install

install.packages('OBsMD')

Monthly Downloads

277

Version

12.0

License

GPL (>= 2)

Maintainer

Marta Nai Ruscone

Last Published

August 19th, 2024

Functions in OBsMD (12.0)

BM93.e1.data

Example 1 data in Box and Meyer (1993)

MetalCutting

Data sets in Edwards, Weese and Palmer (2014)

OBsMD-internal

Internal OBsMD objects

plot.OBsProb

Plotting of Posterior Probabilities from Objective Bayesian Design

combinations

Enumerate the Combinations of the Elements of a Vector

PB12Des

12-run Plackett-Burman Design Matrix

Reactor.data

Reactor Experiment Data

print.OBsProb

Printing Objective Posterior Probabilities from Bayesian Design

print.OMD

Print Optimal OMD Follow-Up Experiments

summary.OBsProb

Summary of Posterior Probabilities from Objective Bayesian Design

summary.OMD

Summary of Optimal OMD Follow-Up Experiments

BM86.data

Data sets in Box and Meyer (1986)

BM93.e3.data

Example 3 data in Box and Meyer (1993)

BM93.e2.data

Example 2 data in Box and Meyer (1993)

OMD

Objective Model Discrimination (OMD) in Follow-Up Experiments

OBsProb

Objective Posterior Probabilities from Bayesian Screening Experiments

OBsMD-package

Objective Bayesian Model Discrimination in Follow-Up Designs

OBsMD.es5