For d=1, if `h`

is missing, the default bandwidth is `hpi`

.
For d>1, if `H`

is missing, the default is `Hpi`

.

For d=1, if `positive=TRUE`

then `x`

is transformed to
`log(x+adj.positive)`

where the default `adj.positive`

is
the minimum of `x`

.

For d=1, 2, 3, 4, and if `eval.points`

is not specified, then the
density estimate is computed over a grid
defined by `gridsize`

(if `binned=FALSE`

) or
by `bgridsize`

(if `binned=TRUE`

). This form is suitable for
visualisation in conjuction with the `plot`

method.

--If `eval.points`

is specified, as a vector (d=1) or
as a matrix (d=2, 3, 4), then the
density estimate is computed at `eval.points`

. This form is
suitable for numerical summaries (e.g. maximum likelilood), and is
not compatible with the `plot`

method.

--For d>4, computing the kernel
density estimate over a grid is not feasible, and so it is computed exactly
and `eval.points`

(as a matrix) must be specified.

Binned kernel estimation is an approximation to the exact kernel
estimation and is available for d=1, 2, 3, 4. This makes
kernel estimators feasible for large samples.

The default `bgridsize,gridsize`

are d=1: 401; d=2: rep(151, 2);
d=3: rep(51, 3); d=4: rep(21,4).

The effective support for a normal kernel is where
all values outside `[-supp,supp]^d`

are zero.

The default `xmin`

is `min(x)-Hmax*supp`

and `xmax`

is `max(x)+Hmax*supp`

where `Hmax`

is the maximum of the
diagonal elements of `H`

. The grid produced is the outer
product of `c(xmin[1], xmax[1])`

, ..., `c(xmin[d], xmax[d])`

.