looney<-function(y,nbs=2000,alpha=0.05) {

############################################################
#
#  looney - get the correlation of the data and their 
#    expected value under normality 
#    estimate the pval and critical value via parametric bootstrap
#    for testing 
#      H0: Data are Normal
#      HA: Data are not Normal
#    ie no need for the Looney Table
#
#  input 
#    y - data you would like tested for normality 
#    nbs - number of bootstrap samples to take
#    alpha - approximates a cv for this size test
#
#  output
#    cor - the correlation between the data and there 
#      expected value under normality
#    pval - a bootstrap estimate of the pvalue for th
#    ecv - the estimated critical value 
#      (should be quite close to the value in the Looney Table)
#
############################################################

	n<-length(y)
	ey<-qnorm((rank(y) - 0.375)/(n+0.25))
	est<-cor(ey,y)

	cors<-rep(0,nbs)

	for( i in 1:nbs ) {

		z<-rnorm(n)
		ez<-qnorm((rank(z) - 0.375)/(n+0.25))

		cors[i]<-cor(ez,z)

	}

	pval=sum( cors < est )/nbs
	ecv=sort(cors)[nbs*alpha]

	list(cor=est, pval=pval, ecv=ecv)

}