FlDeriv                 'FlDeriv'compute the Jacobian of the Fleishman
                        transform for a given set of coefficients b,c,d
Fleishman               'Fleishman' computes the variance, skewness and
                        kurtosis for a given set of of coefficients
                        b,c,d for the Fleishman transform
FleishmanIC             Functions to generate nonnormal distributed
                        multivariate random vectors with mean=0, var=1
                        and given correlations and coefficients of
                        skewness and excess kurtosis. This is done with
                        the method of Vale & Morelli: The coefficients
                        of the Fleishman transform Y = -c + bX +cX^2 +
                        dX^3 are computed.  from given skewness
                        gamma[1] = E(Y^3) and kurtosis gamma[2] =
                        E(Y^4) - 3. A indermediate correlation matrix
                        is computed from the desired correlation matrix
                        and the Fleishman coefficients. A singular
                        value decomposition of the indermediate
                        correlation matrix is performed and a matrix of
                        independend normal random numbers is generated
                        and transformed into correlated ones. Finally
                        the Fleishman transform is applied to the
                        columns of this data matrix.
NewtonFl                'NewtonFl' Newton's method to find roots of the
                        function FlFunc.
SolveCorr               'SolveCorr' Solve the Vale-Maurelli cubic
                        equation to find the intermediate correlation
                        between two normal variables that gives rise to
                        a target correlation (rho) between the two
                        transformed nonnormal variables.
VMTargetCorr            'VMTargetCorr' Given a target correlation
                        matrix, R, and target values of skewness and
                        kurtosis for each marginal distribution, find
                        the "intermediate" correlation matrix, V
averageR2w              For use in boottestgscm.
boottestgscm            Testing two segmentations of a GSC model
checkw                  Checking composite based SE models if there are
                        weights in accordance with the loadings and the
                        covariance matrix of the composites
clustergscairls         Clustering gsc-models
gscals                  Estimating GSC models belonging to scenarios
                        reflective-reflective, formative-reflective and
                        formative-formative
gscalsout               Output of gscals for the simplemodel data.
gscalsresid             For use in clustergscairls, residuals of a
                        gsc-model
gscmcov                 Determination of the covariance matrix of a GSC
                        model belonging to scenario 1, scenario 2,
                        scenario 3
gscmcovff               'gscmcovff' determines the covariance matrix of
                        a GSC model belonging to scenario ff.
gscmcovfr               'gscmcovfr' determines the covariance matrix of
                        a GSC model belonging to scenario fr. The
                        covariance matrices of the errors are supposed
                        to be diagonal.
gscmcovout              Output of covgscmodel for the simplemodel data.
gscmcovrr               'gscmcovrr' determines the covariance matrix of
                        a GSC model belonging to scenario rr.
mobi250                 Mobile phone data for the ECSI model.
plspath                 Estimation of pls-path models
poloecfree              Political and economical freedom.
rValeMaurelli           'rValeMaurelli' Simulate data from a
                        multivariate nonnormal distribution such that
                        1) Each marginal distribution has a specified
                        skewness and kurtosis 2) The marginal variables
                        have the correlation matrix R
simplemodel             Simulated data.
subcheckw               Function for use in checkw
twoclm                  Simulated data.
