Given a
\(n\times k_1 \times k_2 \times ... \times k_l\)
array whose 'rows' are independent observations of \(X\), computes the
\(k_1 \times k_2 \times ... \times k_l\)
array of the mean of \(X\) and the
\(k_1 \times k_2 \times ... \times k_l \times k_1 \times k_2 ... k_l\)
array of the covariance, based on \(n\) observations,
returned as two madness
objects. The variance-covariance
of each is estimated. The two objects have the same 'xtag', and so
may be combined together.
When the diag.only=TRUE
, only the diagonal of the covariance is
computed and returned.
One may use the default method for computing covariance,
via the vcov
function, or via a 'fancy' estimator,
like sandwich:vcovHAC
, sandwich:vcovHC
, etc.