{{alias}}( N, c, x, strideX, out, strideOut ) Computes the mean and standard deviation of a double-precision floating- point strided array. 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 standard deviation 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 standard deviation according to `N - c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the standard deviation 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 corrected sample standard deviation, 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 ) [ ~0.3333, ~2.0817 ] // 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 ) [ ~0.3333, ~2.0817 ] // 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 ) [ ~0.3333, ~2.0817 ] {{alias}}.ndarray( N, c, x, strideX, offsetX, out, strideOut, offsetOut ) Computes the mean and standard deviation of a double-precision floating- point strided array using 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 standard deviation according to `N - c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the standard deviation 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 corrected sample standard deviation, 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 ) [ ~0.3333, ~2.0817 ] // 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 ) [ ~0.3333, ~2.0817 ] See Also --------