NMixMCMC. It computes
  marginal (pairwise bivariate) plug-in densities obtained by using posterior
  summary statistics (e.g., posterior means) of mixture weights, means
  and variances.NMixPlugDensJoint2(x, ...)## S3 method for class 'default':
NMixPlugDensJoint2(x, scale, w, mu, Sigma, \dots)
## S3 method for class 'NMixMCMC':
NMixPlugDensJoint2(x, grid, lgrid=50, scaled=FALSE, \dots)
## S3 method for class 'GLMM_MCMC':
NMixPlugDensJoint2(x, grid, lgrid=50, scaled=FALSE, \dots)
NMixMCMC for
    NMixPlugDensJoint2.NMixMCMC function.    An object of class GLMM_MCMC for
    NMixPlugDensJoint2.GLMM_MCMC function.
    
    A list with the grid values (see below)
shift and the
    scale. If not given, shift is equal to zero and scale is
    equal to one.mu has
    $K$ rows and $p$ columns, where $K$ denotes the number
    of mixture components and $p$ is dimension of the mixture
    distribution.    If grid is not specified, it is created automatically using
    the information from the posterior summary statistics stored in x.
grid if
    that is not specified.TRUE, the density of shifted and scaled data is
    summarized. The shift and scale vector are taken from the
    scale component of the object x.NMixPlugDensJoint2 which has the following components:x1, ...or take names from
    grid argument.1-2,
    1-3, ..., i.e.,
    dens[[1]]$=$dens[["1-2"]] is the pairwise predictive
    density for margins 1 and 2, etc. Each component of the list
    is a matrix in such a form that it can be directly passed together
    with the proper components of x to the plotting functions
    like contour or image.plot method implemented for the resulting object.plot.NMixPlugDensJoint2, NMixMCMC, GLMM_MCMC, NMixPredDensJoint2.