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* @license Apache-2.0
*
* Copyright (c) 2025 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 gsumpw = require( '@stdlib/blas/ext/base/gsumpw' ).ndarray;
var gapxsumpw = require( '@stdlib/blas/ext/base/gapxsumpw' ).ndarray;
var arraylike2object = require( '@stdlib/array/base/arraylike2object' );
var accessors = require( './accessors.js' );
// MAIN //
/**
* Computes the arithmetic mean of a strided array using a two-pass error correction algorithm.
*
* ## Method
*
* - This implementation uses a two-pass approach, as suggested by Neely (1966).
*
* ## References
*
* - Neely, Peter M. 1966. "Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients." _Communications of the ACM_ 9 (7). Association for Computing Machinery: 496–99. doi:[10.1145/365719.365958](https://doi.org/10.1145/365719.365958).
* - Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In _Proceedings of the 30th International Conference on Scientific and Statistical Database Management_. New York, NY, USA: Association for Computing Machinery. doi:[10.1145/3221269.3223036](https://doi.org/10.1145/3221269.3223036).
*
* @param {PositiveInteger} N - number of indexed elements
* @param {NumericArray} x - input array
* @param {integer} strideX - stride length
* @param {NonNegativeInteger} offsetX - starting index
* @returns {number} arithmetic mean
*
* @example
*
* var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];
*
* var v = meanpn( 4, x, 2, 1 );
* // returns 1.25
*/
function meanpn( N, x, strideX, offsetX ) {
var mu;
var c;
var o;
if ( N <= 0 ) {
return NaN;
}
o = arraylike2object( x );
if ( o.accessorProtocol ) {
return accessors( N, o, strideX, offsetX );
}
if ( N === 1 || strideX === 0 ) {
return x[ offsetX ];
}
// Compute an estimate for the meanpn:
mu = gsumpw( N, x, strideX, offsetX ) / N;
// Compute an error term:
c = gapxsumpw( N, -mu, x, strideX, offsetX ) / N;
return mu + c;
}
// EXPORTS //
module.exports = meanpn;
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