Nonparametric BCa confidence limits

USAGE:
bcanon(x, nboot, theta, ..., 
  alpha=c(0.025, 0.05, 0.1, 0.16, 0.84, 0.9, 0.95, 0.975))

ARGUMENTS:
x:     a vector containing the data. To bootstrap   more  complex
       data  structures (e.g bivariate data) see the last example
       below.
nboot:    The number of bootstrap samples desired.
theta:    function to be bootstrapped. Takes x  as  an  argument,
       and  may  take  additional  arguments  (see below and last
       example).
...:   any additional arguments to be passed to theta

alpha:   optional argument specifying confidence levels desired

VALUE:
       list with the following components:

confpoint: estimated bca confidence limits

z0:    estimated bias correction

acc: estimated acceleration constant

u: jackknife influence values

REFERENCES
  Efron, B. and   Tibshirani, R. (1986).  The Bootstrap
 Method for standard errors, confidence intervals,
and other measures of   statistical accuracy.
  Statistical Science, Vol 1., No. 1, pp 1-35.

Efron, B. (1987). Better bootstrap confidence intervals (with discussion).
 J. Amer. Stat. Assoc. vol 82, pg 171

Efron, B. and Tibshirani, R. (1993) An Introduction to the Bootstrap.
Chapman and Hall, New York, London.

EXAMPLES:

#  bca limits for the  mean 
#  (this is for illustration; 
#   since "mean" is a built in function,
#   bcanon(x,100,mean) would be simpler!)

x <- rnorm(20)                
theta <- function(x){mean(x)}
results <- bcanon(x,100,theta)   
                              
# To obtain bca limits for functions of more 
# complex data structures, write theta
# so that its argument x is the set of observation
# numbers and simply pass as data to bcanon 
# the vector 1,2,..n. 
# For example, find bca limits for
# the correlation coefficient from a set of 15 data pairs:

xdata <- matrix(rnorm(30),ncol=2)
n <- 15
theta <- function(x,xdata){ cor(xdata[x,1],xdata[x,2]) }
results <- bcanon(1:n,100,theta,xdata)