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), which evolved in La Rocca (2008), and my talk at UseR! 2008.

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

all software and documentation are available on CRAN (currently not in the Repository but in the Archive).

When the package was available in the Repository (I am sorry but I currently lack the energies to put it back there)

typical installation (including package

R> install.packages("BayHaz", depend = "Suggests")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")