

   MMuullttiivvaarriiaattee BBiinnaarryy RRaannddoomm VVaarriiaatteess

        rmvbin(n, margprob, commonprob=diag(margprob),
               bincorr=diag(length(margprob)),
               sigma=diag(length(margprob)),
               colnames=NULL, simulvals=NULL)

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

        Creates correlated multivariate binary random variables
        by thresholding a normal distribution. The correlations
        of the components can be specified either as common
        probabilities, correlation matrix  of the binary dis-
        tribution, or covariance matrix of the normal distribu-
        tion. Hence, only one of the arguments `commonprob',
        `bincorr' and `sigma' may be specified. Default are
        uncorrelated components.

        `n' samples from a multivariate normal distribution
        with mean and variance chosen in order to get the
        desired margin and common probabilities are sampled.
        Negative values are converted to 0, positive values to
        1.

   AAuutthhoorr((ss))::

        Friedrich Leisch

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

        Friedrich Leisch, Andreas Weingessel and Kurt Hornik
        (1998). On the generation of correlated artificial
        binary data. Working Paper Series, SFB ``Adaptive
        Information Systems and Modelling in Economics and Man-
        agement Science'', Vienna University of Economics,
        <URL: http://www.wu-wien.ac.at/am>

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

        `commonprob2sigma',`check.commonprob', `simul.common-
        prob'

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

        # uncorrelated columns:
        rmvbin(10, margprob=c(0.3,0.9))

        # correlated columns
        m <- cbind(c(1/2,1/5,1/6),c(1/5,1/2,1/6),c(1/6,1/6,1/2))
        rmvbin(10,commonprob=m)

        # same as the second example, but faster if the same probabilities are
        # used repeatedly (coomonprob2sigma rather slow)
        sigma <- commonprob2sigma(m)
        rmvbin(10,marginprob=diag(m),sigma=sigma)

