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NPP (version 0.6.0)

Normalized Power Prior Bayesian Analysis

Description

Posterior sampling in several commonly used distributions using normalized power prior as described in Duan, Ye and Smith (2006) and Ibrahim et.al. (2015) . Sampling of the power parameter is achieved via either independence Metropolis-Hastings or random walk Metropolis-Hastings based on transformation.

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Version

Install

install.packages('NPP')

Monthly Downloads

219

Version

0.6.0

License

GPL (>= 2)

Maintainer

Zifei Han

Last Published

December 12th, 2023

Functions in NPP (0.6.0)

BerOMNPP_MCMC1

MCMC Sampling for Bernoulli Population of multiple ordered historical data using Normalized Power Prior
MultinomialNPP_MCMC

MCMC Sampling for Multinomial Population using Normalized Power Prior
ModeDeltaMultinomialNPP

Calculate Posterior Mode of the Power Parameter in Normalized Power Prior with Grid Search, Multinomial Population
ModeDeltaLMNPP

Calculate Posterior Mode of the Power Parameter in Normalized Power Prior with Grid Search, Normal Linear Model
ModeDeltaPoisNPP

Calculate Posterior Mode of the Power Parameter in Normalized Power Prior with Grid Search, Poisson Population
PoiMNPP_MCMC1

MCMC Sampling for Poisson Population using Normalized Power Prior with Multiple Historical Data
PoissonNPP_MCMC

MCMC Sampling for Bernoulli Population using Normalized Power Prior
loglikBerD0

A Function to Calculate Log-likelihood of the Historical Data, Given Matrix-valued Parameters, for Bernoulli Population
logCdelta

A Function to Interpolate \(logC(\delta)\) Based on Its Values on Selected Knots
ModeDeltaNormalNPP

Calculate Posterior Mode of the Power Parameter in Normalized Power Prior with Grid Search, Normal Population
PHData

PH Data on four sites in Virginia
VaccineData

Dataset of a Vaccine Trial for RotaTeq and Multiple Historical Trials for Control Group
PoiMNPP_MCMC2

MCMC Sampling for Poisson Population of multiple historical data using Normalized Power Prior
loglikNormD0

A Function to Calculate Log-likelihood of the Historical Data, Given Array-valued Parameters, for Normal Population
PoiOMNPP_MCMC2

MCMC Sampling for Poisson Population of multiple ordered historical data using Normalized Power Prior
logCknot

A Function to Calculate \(logC(\delta)\) on Selected Knots
SPDData

Dataset for Diagnostic Test (PartoSure Test, Medical Device) Evaluation for Spontaneous Preterm Delivery
NormalNPP_MCMC

MCMC Sampling for Normal Population using Normalized Power Prior
PoiOMNPP_MCMC1

MCMC Sampling for Poisson Population of multiple ordered historical data using Normalized Power Prior
ModeDeltaBerNPP

Calculate Posterior Mode of the Power Parameter in Normalized Power Prior with Grid Search, Bernoulli Population
LaplacelogC

A Function to Calculate \(logC(\delta)\) Based on Laplace Approximation
LMMNPP_MCMC1

MCMC Sampling for Linear Regression Model of multiple historical data using Normalized Power Prior
BerMNPP_MCMC1

MCMC Sampling for Bernoulli Population with Multiple Historical Data using Normalized Power Prior
BerMNPP_MCMC2

MCMC Sampling for Bernoulli Population of multiple historical data using Normalized Power Prior
BerNPP_MCMC

MCMC Sampling for Bernoulli Population using Normalized Power Prior
LMMNPP_MCMC2

MCMC Sampling for Linear Regression Model of multiple historical data using Normalized Power Prior
LMOMNPP_MCMC1

MCMC Sampling for Linear Regression Model of multiple historical data using Ordered Normalized Power Prior
LMOMNPP_MCMC2

MCMC Sampling for Linear Regression Model of multiple historical data using Ordered Normalized Power Prior
LMNPP_MCMC

MCMC Sampling for Normal Linear Model using Normalized Power Prior
BerOMNPP_MCMC2

MCMC Sampling for Bernoulli Population of multiple ordered historical data using Normalized Power Prior