This function returns the most recent covariance matrix or a list of
blocking covariance matrices from an object of class `demonoid`

,
the most recent covariance matrix from `iterquad`

,
`laplace`

, or `vb`

, the most recent covariance matrix from
the chain with the lowest deviance in an object of class
`demonoid.hpc`

, and a number of covariance matrices of an object
of class `pmc`

equal to the number of mixture components. The
returned covariance matrix or matrices are intended to be the initial
proposal covariance matrix or matrices for future updates. A variance
vector from an object of class `demonoid`

or `demonoid.hpc`

is converted to a covariance matrix.

`as.covar(x)`

x

This is an object of class `demonoid`

,
`demonoid.hpc`

, `iterquad`

, `laplace`

, `pmc`

, or
`vb`

.

The returned value is a matrix (or array in the case of PMC with multiple mixture components) of the latest observed or proposal covariance, which may now be used as an initial proposal covariance matrix or matrices for a future update.

Unless it is known beforehand how many iterations are required for
iterative quadrature, Laplace Approximation, or Variational Bayes to
converge, MCMC to appear converged, or the normalized perplexity to
stabilize in PMC, multiple updates are necessary. An additional
update, however, should not begin with the same proposal covariance
matrix or matrices as the original update, because it will have to
repeat the work already accomplished. For this reason, the
`as.covar`

function may be used at the end of an update to change
the previous initial values to the latest values.

The `as.covar`

function is most helpful with objects of class
`pmc`

that have multiple mixture components. For more
information, see `PMC`

.

`IterativeQuadrature`

,
`LaplaceApproximation`

,
`LaplacesDemon`

,
`LaplacesDemon.hpc`

,
`PMC`

, and
`VariationalBayes`

.