Heligman Pollard mortality model parameter estimation using Bayesian Melding with Incremental Mixture Importance Sampling

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Documentation for package ‘HPbayes’ version 0.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

HPbayes-package	Heligman Pollard mortality model parameter estimation using Bayesian Melding with Incremental Mixture Importance Sampling
age	prior, age groups, lx and dx values
dens.prior	Density of the prior
dx	prior, age groups, lx and dx values
entropy.wts	Entropy of the rescaled weights relative to uniformity
expt.upts	Expected number of unique inputs after the final IMIS re-sample
final.resamp	Final re-sampling step in Bayesian Melding using IMIS
hp.bm.imis	Heligman-Pollard parameter estimator using Bayesian Melding with Incremental Mixture Importance Sampling
hp.lifetab	Heligman-Pollard life table conversion
hp.nqx	Heligman-Pollard parameter conversion to age-specifc probabilites of death
HPbayes	Heligman Pollard mortality model parameter estimation using Bayesian Melding with Incremental Mixture Importance Sampling
hpbayes.plot	Bayesian Melding posterior Heligman-Pollard parameter distribution plot
like.resamp	Local Optimums and Covariance from the optimizer step
ll.binom	Binomial likelihood
loop.optim	Optimizer step for estimating the Heligman-Pollard Parameters using the Bayesian Melding with IMIS-opt procedure
lx	prior, age groups, lx and dx values
mod8p	Heligman-Pollard parameter coversion to age-specific probabilites of death
mp8.ll	Binomial likelihood for a set of Heligman-Pollard Parameters
postpri.plot	Posterior/Prior Heligman-Pollard parameter distribution plot
pri.mle	Heligman-Pollard parameter prior formation for use with Bayesian Melding using IMIS
prior.form	Heligman-Pollard parameter prior formation
prior.likewts	Prior likelihoods and weights
q0	prior, age groups, lx and dx values
samp.postopt	Multivariate Gaussian Sampling for Heligman-Pollard model estimated via Bayesian Melding
var.rwts	Variance of the rescaled weights when estimating the Heligman-Pollard parameters using Bayesian Melding with IMIS