00readme.txt 
A read me first file that describe the models, data set, and
rest of files. 
DeerEcervi.txt 
Original Deer data set from the ZIWSS book. 
01max_pql.r 
Maximize penalized quasilikelihood for GLMM. 
11npbs_for.r 
 Bootstrap confidence interval for standard deviation of random
effects.
 Using the
for() loop.

12npbs_lapply.r 
 Bootstrap confidence interval for standard deviation of random effects.
 Using the
lapply() function.

13npbs_mclapply.r 
 Bootstrap confidence interval for standard deviation of random
effects.
 Using the
mclapply() function from the
parallel package.
 Parallelization is done in the level of multiple independent
bootstrap samples.

14npbs_pbdR.r 
 Bootstrap confidence interval for standard deviation of
random effects.
 Using the
task.pull() functions from the
pbdMPI package.
 Parallelization is done in the level of multiple independent
bootstrap samples.

21mcmc_glm.r 
 MCMC approach for GLM (no random effects).
 Using the
lapply() function.

22mcmc_glm_mclapply.r 
 MCMC approach for GLM (no random effects).
 Using the
mclapply() function from the
parallel package.
 Parallelization is done in the level of multiple independent
chains.

23mcmc_glm_pbdR.r 
 MCMC approach for GLM (no random effects).
 Using the
task.pull() functions from the
pbdMPI package.
 Parallelization is done in the level of multiple independent
chains.

31mcmc_glmm.r 
MCMC approach for GLMM (random intercepts). 
41mcmc_glmm_mclapply.r 
 MCMC approach for GLMM (random intercepts).
 Using the
mclapply() function from the
parallel package.
 Parallelization is done within the iterations of MCMC.
 See u4mcmc_glmm_mclapply.r for details.

42mcmc_glmm_pbdR.r 
 MCMC approach for GLMM (random intercepts).
 Using the
task.pull() functions from the
pbdMPI package.
 Parallelization is done within the iterations of MCMC.
 See u4mcmc_glmm_pbdR.r for details.

u0deer.r 
Utility. 
u1npbs.r 
Utility. 
u2mcmc_glm.r 
Utility. 
u3mcmc_glmm.r 
Utility. 
u4mcmc_glmm_mclapply.r 
Utility. 
u4mcmc_glmm_pbdR.r 
Utility. 