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* @license Apache-2.0
*
* Copyright (c) 2026 The Stdlib Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
'use strict';
// MODULES //
var isnan = require( '@stdlib/math/base/assert/is-nan' );
var dsumpw = require( '@stdlib/blas/ext/base/dsumpw' ).ndarray;
// MAIN //
/**
* Computes the mean and variance of a double-precision floating-point strided array using Welford's algorithm.
*
* @param {PositiveInteger} N - number of indexed elements
* @param {number} correction - degrees of freedom adjustment
* @param {Float64Array} x - input array
* @param {integer} strideX - `x` stride length
* @param {NonNegativeInteger} offsetX - `x` starting index
* @param {Float64Array} out - output array
* @param {integer} strideOut - `out` stride length
* @param {NonNegativeInteger} offsetOut - `out` starting index
* @returns {Float64Array} output array
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
*
* var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
* var out = new Float64Array( 2 );
*
* var v = dmeanvarwd( 4, 1, x, 2, 1, out, 1, 0 );
* // returns <Float64Array>[ 1.25, 6.25 ]
*/
function dmeanvarwd( N, correction, x, strideX, offsetX, out, strideOut, offsetOut ) { // eslint-disable-line max-len
var mu;
var ix;
var io;
var M2;
var M;
var d;
var c;
var n;
var i;
ix = offsetX;
io = offsetOut;
if ( N <= 0 ) {
out[ io ] = NaN;
out[ io+strideOut ] = NaN;
return out;
}
n = N - correction;
if ( N === 1 || strideX === 0 ) {
out[ io ] = x[ ix ];
if ( n <= 0.0 ) {
out[ io+strideOut ] = NaN;
} else {
out[ io+strideOut ] = 0.0;
}
return out;
}
// Compute an estimate for the mean:
mu = dsumpw( N, x, strideX, offsetX ) / N;
if ( isnan( mu ) ) {
out[ io ] = NaN;
out[ io+strideOut ] = NaN;
return out;
}
// Compute the sum of squared differences from the mean...
M2 = 0.0;
M = 0.0;
for ( i = 0; i < N; i++ ) {
d = x[ ix ] - mu;
M2 += d * d;
M += d;
ix += strideX;
}
// Compute an error term for the mean:
c = M / N;
out[ io ] = mu + c;
if ( n <= 0.0 ) {
out[ io+strideOut ] = NaN;
} else {
out[ io+strideOut ] = (M2/n) - (c*(M/n));
}
return out;
}
// EXPORTS //
module.exports = dmeanvarwd;
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