Base Statistics

Base (i.e., lower-level) statistical functions.

Installation

npm install @stdlib/stats-base

Usage

var stats = require( '@stdlib/stats-base' );

stats

Namespace containing "base" (i.e., lower-level) statistical functions.

var ns = stats;
// returns {...}

The namespace contains the following sub-namespaces:

• dists: base (i.e., lower-level) probability distribution modules.

The namespace contains the following statistical functions:

• cumax( N, x, strideX, y, strideY ): calculate the cumulative maximum of a strided array.
• cumaxabs( N, x, strideX, y, strideY ): calculate the cumulative maximum absolute value of a strided array.
• cumin( N, x, strideX, y, strideY ): calculate the cumulative minimum of a strided array.
• cuminabs( N, x, strideX, y, strideY ): calculate the cumulative minimum absolute value of a strided array.
• ndarray: base ndarray statistical functions.
• sdsnanmean( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values and using extended accumulation.
• snanstdev( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoring NaN values.
• snanstdevch( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm.
• snanstdevpn( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoring NaN values and using a two-pass algorithm.
• snanstdevtk( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoring NaN values and using a one-pass textbook algorithm.
• snanstdevwd( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoring NaN values and using Welford's algorithm.
• snanstdevyc( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoring NaN values and using a one-pass algorithm proposed by Youngs and Cramer.
• snanvariance( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoring NaN values.
• snanvariancech( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm.
• snanvariancepn( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoring NaN values and using a two-pass algorithm.
• snanvariancetk( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoring NaN values and using a one-pass textbook algorithm.
• snanvariancewd( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoring NaN values and using Welford's algorithm.
• snanvarianceyc( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoring NaN values and using a one-pass algorithm proposed by Youngs and Cramer.

Examples

var objectKeys = require( '@stdlib/utils-keys' );
var ns = require( '@stdlib/stats-base' );

console.log( objectKeys( ns ) );

Notice

This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.

Community

License

See LICENSE.

Copyright

Copyright © 2016-2026. The Stdlib Authors.