NMixMCMC. It computes
  (posterior predictive) estimates of univariate conditional densities.NMixPredCondDensMarg(x, ...)## S3 method for class 'default':
NMixPredCondDensMarg(x, icond, prob, scale, K, w, mu, Li, Krandom=FALSE, ...)
## S3 method for class 'NMixMCMC':
NMixPredCondDensMarg(x, icond, prob, grid, lgrid=50, scaled=FALSE, \dots)
## S3 method for class 'GLMM_MCMC':
NMixPredCondDensMarg(x, icond, prob, grid, lgrid=50, scaled=FALSE, \dots)
NMixMCMC for
    NMixPredCondDensMarg.NMixMCMC function.    An object of class GLMM_MCMC for
    NMixPredCondDensMarg.GLMM_MCMC function.
    
    A list with the grid values (see be
prob. These can be used to draw
    pointwise credible intervals.shift and the
    scale. If not given, shift is equal to zero and scale is
    equal to one.Krandom$=$FALSE) or a
    numeric vector with the chain for the number of mixture components.grid[[icond]]
    determines the values by which we condition.    If grid is not specified, it is created automatically usin
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.NMixPredCondDensMarg which has the following components:x1, ...or take names from
    grid argument.x[[icond]]. Each dens[[j]] is again a list
    with conditional densities for each margin given margin
    icond equal to x[[icond]][j].
    The value of dens[[j]][[imargin]] gives a value
    of a marginal density of the imargin-th margin at x[[icond]][j].prob.prob is given then there is one
    additional component named prob which has the same structure as the
    component dens and keeps computed posterior pointwise
    quantiles.plot method implemented for the resulting object.plot.NMixPredCondDensMarg, NMixMCMC, GLMM_MCMC.