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OBsMD (version 11.1)

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

233

Version

11.1

License

GPL (>= 2)

Maintainer

Marta Nai Ruscone

Last Published

November 14th, 2023

Functions in OBsMD (11.1)

print.OMD

Print Optimal OMD Follow-Up Experiments

OBsProb

Objective Posterior Probabilities from Bayesian Screening Experiments

print.OBsProb

Printing Objective Posterior Probabilities from Bayesian Design

OMD

Objective Model Discrimination (OMD) in Follow-Up Experiments

summary.OMD

Summary of Optimal OMD Follow-Up Experiments

combinations

Enumerate the Combinations of the Elements of a Vector

summary.OBsProb

Summary of Posterior Probabilities from Objective Bayesian Design

plot.OBsProb

Plotting of Posterior Probabilities from Objective Bayesian Design

PB12Des

12-run Plackett-Burman Design Matrix

Reactor.data

Reactor Experiment Data

BM86.data

Data sets in Box and Meyer (1986)

BM93.e1.data

Example 1 data in Box and Meyer (1993)

BM93.e3.data

Example 3 data in Box and Meyer (1993)

BM93.e2.data

Example 2 data in Box and Meyer (1993)

OBsMD-internal

Internal OBsMD objects

OBsMD-package

Objective Bayesian Model Discrimination in Follow-Up Designs

MetalCutting

Data sets in Edwards, Weese and Palmer (2014)

OBsMD.es5