text: /**
  * @license Apache-2.0
  *
  * Copyright (c) 2020 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 dsumpw = require( '@stdlib/blas/ext/base/dsumpw' ).ndarray;
  var dapxsumpw = require( '@stdlib/blas/ext/base/dapxsumpw' ).ndarray;
  
  
  // MAIN //
  
  /**
  * Computes the arithmetic mean of a double-precision floating-point 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 {Float64Array} x - input array
  * @param {integer} stride - stride length
  * @param {NonNegativeInteger} offset - starting index
  * @returns {number} arithmetic mean
  *
  * @example
  * var Float64Array = require( '@stdlib/array/float64' );
  * var floor = require( '@stdlib/math/base/special/floor' );
  *
  * var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
  * var N = floor( x.length / 2 );
  *
  * var v = dmeanpn( N, x, 2, 1 );
  * // returns 1.25
  */
  function dmeanpn( N, x, stride, offset ) {
  	var mu;
  	var c;
  
  	if ( N <= 0 ) {
  		return NaN;
  	}
  	if ( N === 1 || stride === 0 ) {
  		return x[ offset ];
  	}
  	// Compute an estimate for the mean:
  	mu = dsumpw( N, x, stride, offset ) / N;
  
  	// Compute an error term:
  	c = dapxsumpw( N, -mu, x, stride, offset ) / N;
  
  	return mu + c;
  }
  
  
  // EXPORTS //
  
  module.exports = dmeanpn;
  