dclone-package: Data Cloning
Description
Low level functions for implementing
maximum likelihood estimating procedures for
complex models using data cloning and Bayesian
Markov chain Monte Carlo methods with support
for JAGS, WinBUGS and OpenBUGS.
Parallel MCMC computation is supported
and can result in considerable speed-up.
Main functions include:
jags.fit, bugs.fit:
conveniently fit BUGS models.
(jags.parfit fits chains on
parallel workers for JAGS.)dc.fit: iterative model fitting by
the data cloning algorithm.
(dc.parfit is the parallelized version.)dctable, dcdiag:
helps evaluating data cloning
convergence by descriptive statistics and diagnostic tools.
(These are based on e.g. chisq.diag
and lambdamax.diag.)coef.mcmc.list, confint.mcmc.list.dc,
dcsd.mcmc.list, quantile.mcmc.list,
vcov.mcmc.list.dc, mcmcapply,
stack.mcmc.list:
methods for mcmc.list objects.write.jags.model, clean.jags.model,
custommodel:
convenient functions for handling BUGS models.jagsModel, codaSamples: basic functions
from rjags package rewrote to recognize data cloning
attributes from data (parJagsModel,
parUpdate, parCodaSamples
are the parallel versions).References
Solymos, P., 2010. dclone: Data Cloning in R.
The R Journal 2(2), 29--37.
URL: http://journal.r-project.org/archive/2010-2/RJournal_2010-2_Solymos.pdf
Lele, S.R., B. Dennis and F. Lutscher, 2007.
Data cloning: easy maximum likelihood estimation for complex
ecological models using Bayesian Markov chain Monte Carlo methods.
Ecology Letters 10, 551--563.
Lele, S. R., K. Nadeem and B. Schmuland, 2010.
Estimability and likelihood inference for generalized
linear mixed models using data cloning.
Journal of the American Statistical Association
105, 1617--1625.