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BayesGOF (version 5.2)

Bayesian Modeling via Frequentist Goodness-of-Fit

Description

A Bayesian data modeling scheme that performs four interconnected tasks: (i) characterizes the uncertainty of the elicited parametric prior; (ii) provides exploratory diagnostic for checking prior-data conflict; (iii) computes the final statistical prior density estimate; and (iv) executes macro- and micro-inference. Primary reference is Mukhopadhyay, S. and Fletcher, D. 2018 paper "Generalized Empirical Bayes via Frequentist Goodness of Fit" ().

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Version

Install

install.packages('BayesGOF')

Monthly Downloads

326

Version

5.2

License

GPL-2

Maintainer

Doug Fletcher

Last Published

October 9th, 2018

Functions in BayesGOF (5.2)

gMLE.bb

Beta-Binomial Parameter Estimation
gMLE.pg

Negative-Binomial Parameter Estimation
DS.sampler

Samples data from DS(G,m) distribution.
gMLE.nn

Normal-Normal Parameter Estimation
surg

Intestinal surgery data
DS.prior

Prior Diagnostics and Estimation
steroid

Nasal Steroid Data
terb

Terbinafine trial data
tacks

Rolling Tacks Data
NorbergIns

Norberg life insurance data
galaxy

Galaxy Data
rat

Rat Tumor Data
ship

Portsmouth Navy Shipyard Data
ulcer

Recurrent Bleeding of Ulcers
arsenic

Arsenic levels in oyster tissue
gLP.basis

Determine LP basis functions for prior distribution \(g\)
DS.posterior.reduce

Posterior Expectation and Modes of DS object
DS.entropy

Full and Excess Entropy of DS(G,m) prior
ChildIll

Frequency of child illness
DS.macro.inf

Execute MacroInference (mean or mode) on a DS object
AutoIns

Number of claims on an insurance policy
BayesGOF-package

BayesGOF
CorbBfly

Corbet's Butterfly data
DS.Finite.Bayes

Conduct Finite Bayes Inference on a DS object
DS.micro.inf

MicroInference for DS Prior Objects