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Use labeled_comprehension directly in more function in ndmeasure (#74)

* Use NumPy's `mean` with `labeled_comprehension`

When computing a label's mean, NumPy's `mean` function can be used
directly with `labeled_comprehension`. As `labeled_comprehension` is
already being used by `mean` under the hood given that is what `sum`
uses. There is no reason not to just use `labeled_comprehension`
directly in `mean` with the function that we would like to compute.
After all each call to the user provided function includes all relevant
values represented by the label. So NumPy's `mean` is accurate in this
case. Should simplify the Dask graph of `mean` and the computation time
needed. Also should benefit any other function using `mean`.

* Use NumPy's `var` with `labeled_comprehension`

When computing a label's variance, NumPy's `var` function can be used
directly with `labeled_comprehension`. As `labeled_comprehension` is
already being used by `variance` under the hood given that is what `sum`
uses. There is no reason not to just use `labeled_comprehension`
directly in `variance` with the function that we would like to compute.
After all each call to the user provided function includes all relevant
values represented by the label. So NumPy's `var` is accurate in this
case. Should simplify the Dask graph of `variance` and the computation
time needed. Also should benefit any other function using `variance`.

* Use NumPy's `std` with `labeled_comprehension`

When computing a label's standard deviation, NumPy's `std` function can
be used directly with `labeled_comprehension`. As
`labeled_comprehension` is already being used by `standard_deviation`
under the hood given that is what `var` uses. There is no reason not to
just use `labeled_comprehension` directly in `standard_deviation` with
the function that we would like to compute.  After all each call to the
user provided function includes all relevant values represented by the
lab... (continued)

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