# NPP v0.1.0

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## Normalized Power Prior Bayesian Analysis

Posterior sampling in several commonly used distributions using normalized power prior as described in Duan, Ye and Smith (2006) <doi:10.1002/env.752> and Ibrahim et.al. (2015) <doi:10.1002/sim.6728>. Sampling of the power parameter is achieved via either independence Metropolis-Hastings or random walk Metropolis-Hastings based on transformation.

## Functions in NPP

 Name Description PoissonNPP_MCMC MCMC Sampling for Bernoulli Population using Normalized Power Prior PHData PH Data on four sites in Virginia NormalNPP_MCMC MCMC Sampling for Normal Population using Normalized Power Prior LaplacelogC A Function to Calculate $logC(\delta)$ Based on Laplace Approximation BerNPP_MCMC MCMC Sampling for Bernoulli Population using Normalized Power Prior ModeDeltaPoisNPP Calculate Posterior Mode of the Power Parameter in Normalized Power Prior with Grid Search, Poisson Population ModeDeltaBerNPP Calculate Posterior Mode of the Power Parameter in Normalized Power Prior with Grid Search, Bernoulli Population ModeDeltaNormalNPP Calculate Posterior Mode of the Power Parameter in Normalized Power Prior with Grid Search, Normal Population ModeDeltaMultinomialNPP Calculate Posterior Mode of the Power Parameter in Normalized Power Prior with Grid Search, Multinomial Population MultinomialNPP_MCMC MCMC Sampling for Multinomial Population using Normalized Power Prior loglikBerD0 A Function to Calculate Log-likelihood of the Historical Data, Given Matrix-valued Parameters, for Bernoulli Population SPDData Dataset for Diagnostic Test (PartoSure Test, Medical Device) Evaluation for Spontaneous Preterm Delivery VaccineData Dataset of a Vaccine Trial for RotaTeq and Multiple Historical Trials for Control Group logCknot A Function to Calculate $logC(\delta)$ on Selected Knots logCdelta A Function to Interpolate $logC(\delta)$ Based on Its Values on Selected Knots loglikNormD0 A Function to Calculate Log-likelihood of the Historical Data, Given Array-valued Parameters, for Normal Population No Results!