by Luca La Rocca

This is a software library for Bayesian estimation of smooth hazard rates

via Compound Poisson Process (CPP) priors; see La Rocca (2005).

It comes under the terms of GNU General Public License for use with

all software and documentation are available on CRAN.

Typical installation (including package

R> install.packages("BayHaz", depend = "Suggests")and example usage is as follows:

# load package library(BayHaz) # set RNG seed (for example reproducibility only) set.seed(1234) # select a CPP prior distribution (with default number of CPP jumps) hypars<-CPPpriorElicit(r0 = 0.1, H = 1, T00 = 50, M00 = 2, extra = 0) # plot pointwise prior mean hazard rate and +/- one standard deviation band CPPplotHR(CPPpriorSample(ss = 0, hyp = hypars), tu = "Year")

# load a data set data(earthquakes) # generate a posterior sample # WARNING: THIS IS TIME CONSUMING # about 4 hours on my iBook G4 post<-CPPpostSample(hypars, times = earthquakes$ti, obs = earthquakes$ob, mclen = 10000, burnin = 50000, thin = 20) # check that no additional CPP jumps are needed # IT IS WISE TO TRY THIS ON A SHORT RUN FIRST # if this probability is not negligible, go back to prior selection stage and increase 'extra' ecdf(post$sgm[,post$hyp$F])(post$hyp$T00+3*post$hyp$sd) # gives about 0.058 # take advantage of package 'coda' for output diagnostics MCMCpost<-CPPpost2mcmc(post) # package 'coda' is automatically loaded pdf("diagnostics.pdf") traceplot(MCMCpost) autocorr.plot(MCMCpost, lag.max = 5) par(las = 2) # for better readability of the cross-correlation plot crosscorr.plot(MCMCpost) dev.off() # produces a non-compressed PDF file...find here a compressed version...

# plot some posterior hazard rate summaries CPPplotHR(post , tu = "Year")