R version 3.1.1 RC (2014-07-04 r66081) -- "Sock it to Me"
Copyright (C) 2014 The R Foundation for Statistical Computing
Platform: x86_64-unknown-linux-gnu (64-bit)

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> library(parallel)
> 
> library(boot)
> cd4.rg <- function(data, mle) MASS::mvrnorm(nrow(data), mle$m, mle$v)
> cd4.mle <- list(m = colMeans(cd4), v = var(cd4))
> mc <- getOption("mc.cores", 2)
> run1 <- function(...) boot(cd4, corr, R = 500, sim = "parametric",
+                            ran.gen = cd4.rg, mle = cd4.mle)
> ## To make this reproducible:
> set.seed(123, "L'Ecuyer"); mc.reset.stream()
> res <- mclapply(seq_len(mc), run1)
> cd4.boot <- do.call(c, res)
> boot.ci(cd4.boot,  type = c("norm", "basic", "perc"),
+         conf = 0.9, h = atanh, hinv = tanh)
BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
Based on 1000 bootstrap replicates

CALL : 
boot.ci(boot.out = cd4.boot, conf = 0.9, type = c("norm", "basic", 
    "perc"), h = atanh, hinv = tanh)

Intervals : 
Level      Normal              Basic              Percentile     
90%   ( 0.4575,  0.8547 )   ( 0.4703,  0.8575 )   ( 0.4964,  0.8663 )  
Calculations on Transformed Scale;  Intervals on Original Scale
> 
> proc.time()
   user  system elapsed 
  0.320   0.032   0.351