

   iinntteeggrraatteedd ssqquuaarreedd eerrrroorr bbeettwweeeenn aa ddeennssiittyy eessttiimmaattee aanndd aa
   NNoorrmmaall ddeennssiittyy

        nise(y, hmult=1)

   AArrgguummeennttss::

          y: a vector of data.

      hmult: a factor which can be used to multiply the normal
             optimal smoothing parameter before construction of
             the density estimate.

   DDeessccrriippttiioonn::

        This function evaluates the integrated squared error
        between a density estimate constructed from a standard-
        ised version of the univariate data `y' and a standard
        normal density function.

   DDeettaaiillss::

        the data `y' are first standardised to have sample mean
        0 and sample variance 1.  The integrated squared error
        between a density estimate constructed from these stan-
        dardised data and a standard normal distribution is
        then evaluated.

        see Section 2.5 of the reference below.

   VVaalluuee::

        the integrated squared error.

   SSiiddee EEffffeeccttss::

        none.

   RReeffeerreenncceess::

        Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing
        Techniques for Data Analysis: the Kernel Approach with
        S-Plus Illustrations.  Oxford University Press, Oxford.

   SSeeee AAllssoo::

        `nmise'

   EExxaammpplleess::

        x <- rnorm(x)
        nise(x)

