_N_o_n-_p_a_r_a_m_e_t_r_i_c _b_o_o_t_s_t_r_a_p_p_i_n_g
	
	     bootstrap(x,nboot,theta,..., func=NULL)
	
	_A_r_g_u_m_e_n_t_s:
	
	            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).
	
	         func: (optional) argument specifying the functional
	               the distribution of thetahat that is desired.
	               If func is specified, the jackknife after-
	               bootstrap estimate of its standard error is
	               also returned. See example below.
	
	_V_a_l_u_e_s:
	
	     list with the following components:
	
	thetastar: the nboot bootstrap values of theta
	
	func.thetastar: the functional func of the bootstrap distri-
	          bution of thetastar, if func was specified
	
	jack.boot.val: the jackknife-after-bootstrap values for
	          func, if func was specified
	
	jack.boot.se: the jackknife-after-bootstrap standard error
	          estimate of func, if func was specified
	
	_R_e_f_e_r_e_n_c_e_s:
	
	     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. (1992) Jackknife-after-bootstrap standard
	     errors and influence functions. J. Roy. Stat. Soc. B,
	     vol 54, pages 83-127
	
	     Efron, B. and Tibshirani, R. (1993) An Introduction to
	     the Bootstrap.  Chapman and Hall, New York, London.
	
	_E_x_a_m_p_l_e_s:
	
	     # 100 bootstraps of the sample mean
	     # (this is for illustration;  since "mean" is  a
	     # built in function, bootstrap(x,100,mean) would be simpler!)
	     x <- rnorm(20)
	     theta <- function(x)mean(x)
	
	     results <- bootstrap(x,100,theta)
	
	     # as above, but also estimate the 95th percentile
	     # of the bootstrap dist'n of the mean, and
	     # its jackknife-after-bootstrap  standard error
	
	     perc95 <- function(x)quantile(x, .95)
	
	     results <-  bootstrap(x,100,theta, func=perc95)
	
	     # To bootstrap 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 bootstrap the vector 1,2,..n.
	     # For example, to bootstrap
	     # 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 <- bootstrap(1:n,20,theta,xdata)