time-to-botec/js/node_modules/@stdlib/stats/base/dmeanvarpn/docs/repl.txt
NunoSempere b6addc7f05 feat: add the node modules
Necessary in order to clearly see the squiggle hotwiring.
2022-12-03 12:44:49 +00:00

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{{alias}}( N, c, x, strideX, out, strideOut )
Computes the mean and variance of a double-precision floating-point strided
array using a two-pass algorithm.
The `N` and `stride` parameters determine which elements are accessed at
runtime.
Indexing is relative to the first index. To introduce an offset, use a typed
array view.
If `N <= 0`, the function returns a mean and variance equal to `NaN`.
Parameters
----------
N: integer
Number of indexed elements.
c: number
Degrees of freedom adjustment. Setting this parameter to a value other
than `0` has the effect of adjusting the divisor during the calculation
of the variance according to `N - c` where `c` corresponds to the
provided degrees of freedom adjustment. When computing the variance of a
population, setting this parameter to `0` is the standard choice (i.e.,
the provided array contains data constituting an entire population).
When computing the unbiased sample variance, setting this parameter to
`1` is the standard choice (i.e., the provided array contains data
sampled from a larger population; this is commonly referred to as
Bessel's correction).
x: Float64Array
Input array.
strideX: integer
Index increment for `x`.
out: Float64Array
Output array.
strideOut: integer
Index increment for `out`.
Returns
-------
out: Float64Array
Output array.
Examples
--------
// Standard Usage:
> var x = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 2.0 ] );
> var out = new {{alias:@stdlib/array/float64}}( 2 );
> {{alias}}( x.length, 1, x, 1, out, 1 )
<Float64Array>[ ~0.3333, ~4.3333 ]
// Using `N` and `stride` parameters:
> x = new {{alias:@stdlib/array/float64}}( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );
> out = new {{alias:@stdlib/array/float64}}( 2 );
> var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
> {{alias}}( N, 1, x, 2, out, 1 )
<Float64Array>[ ~0.3333, ~4.3333 ]
// Using view offsets:
> var x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, 1.0 ] );
> var x1 = new {{alias:@stdlib/array/float64}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 );
> N = {{alias:@stdlib/math/base/special/floor}}( x0.length / 2 );
> out = new {{alias:@stdlib/array/float64}}( 2 );
> {{alias}}( N, 1, x1, 2, out, 1 )
<Float64Array>[ ~0.3333, ~4.3333 ]
{{alias}}.ndarray( N, c, x, strideX, offsetX, out, strideOut, offsetOut )
Computes the mean and variance of a double-precision floating-point strided
array using a two-pass algorithm and alternative indexing semantics.
While typed array views mandate a view offset based on the underlying
buffer, the `offset` parameter supports indexing semantics based on a
starting index.
Parameters
----------
N: integer
Number of indexed elements.
c: number
Degrees of freedom adjustment. Setting this parameter to a value other
than `0` has the effect of adjusting the divisor during the calculation
of the variance according to `N - c` where `c` corresponds to the
provided degrees of freedom adjustment. When computing the variance of a
population, setting this parameter to `0` is the standard choice (i.e.,
the provided array contains data constituting an entire population).
When computing the unbiased sample variance, setting this parameter to
`1` is the standard choice (i.e., the provided array contains data
sampled from a larger population; this is commonly referred to as
Bessel's correction).
x: Float64Array
Input array.
strideX: integer
Index increment for `x`.
offsetX: integer
Starting index for `x`.
out: Float64Array
Output array.
strideOut: integer
Index increment for `out`.
offsetOut: integer
Starting index for `out`.
Returns
-------
out: Float64Array
Output array.
Examples
--------
// Standard Usage:
> var x = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 2.0 ] );
> var out = new {{alias:@stdlib/array/float64}}( 2 );
> {{alias}}.ndarray( x.length, 1, x, 1, 0, out, 1, 0 )
<Float64Array>[ ~0.3333, ~4.3333 ]
// Using offset parameter:
> var x = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, 1.0 ] );
> var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
> out = new {{alias:@stdlib/array/float64}}( 2 );
> {{alias}}.ndarray( N, 1, x, 2, 1, out, 1, 0 )
<Float64Array>[ ~0.3333, ~4.3333 ]
See Also
--------