function std
std(
x: array,
flag?: 0 | 1,
dim?: 0 | 1,
): number

Computes the standard deviation.

Computes the standard deviation (square root of variance) for arrays or matrices. Supports both population (N) and sample (N-1) normalizations.

Examples

Sample standard deviation

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([1, 2, 3, 4, 5]), 1.5811388300841898);

Population standard deviation

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([1, 2, 3, 4, 5], 0), 1.4142135623730951);

Matrix standard deviation along rows (default dim=0, flag=1)

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([[1, 2], [3, 4]]), [[0.7071067811865476, 0.7071067811865476]]);

Parameters

x: array

Input array or matrix

optional
flag: 0 | 1

Normalization type (0: population, 1: sample). Default is 1

optional
dim: 0 | 1

Dimension to operate on (0: rows, 1: columns). Default is 0

Return Type

number

Computed standard deviation values

Throws

When input is invalid

std(
x: matrix,
flag?: 0 | 1,
dim?: 0 | 1,
): matrix

Computes the standard deviation.

Computes the standard deviation (square root of variance) for arrays or matrices. Supports both population (N) and sample (N-1) normalizations.

Examples

Sample standard deviation

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([1, 2, 3, 4, 5]), 1.5811388300841898);

Population standard deviation

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([1, 2, 3, 4, 5], 0), 1.4142135623730951);

Matrix standard deviation along rows (default dim=0, flag=1)

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([[1, 2], [3, 4]]), [[0.7071067811865476, 0.7071067811865476]]);

Parameters

Input array or matrix

optional
flag: 0 | 1

Normalization type (0: population, 1: sample). Default is 1

optional
dim: 0 | 1

Dimension to operate on (0: rows, 1: columns). Default is 0

Return Type

Computed standard deviation values

Throws

When input is invalid

std(
x: array,
flag: 0 | 1,
dim?: 0 | 1,
): number

Computes the standard deviation.

Computes the standard deviation (square root of variance) for arrays or matrices. Supports both population (N) and sample (N-1) normalizations.

Examples

Sample standard deviation

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([1, 2, 3, 4, 5]), 1.5811388300841898);

Population standard deviation

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([1, 2, 3, 4, 5], 0), 1.4142135623730951);

Matrix standard deviation along rows (default dim=0, flag=1)

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([[1, 2], [3, 4]]), [[0.7071067811865476, 0.7071067811865476]]);

Parameters

x: array

Input array or matrix

flag: 0 | 1

Normalization type (0: population, 1: sample). Default is 1

optional
dim: 0 | 1

Dimension to operate on (0: rows, 1: columns). Default is 0

Return Type

number

Computed standard deviation values

Throws

When input is invalid

std(
x: matrix,
flag: 0 | 1,
dim?: 0 | 1,
): matrix

Computes the standard deviation.

Computes the standard deviation (square root of variance) for arrays or matrices. Supports both population (N) and sample (N-1) normalizations.

Examples

Sample standard deviation

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([1, 2, 3, 4, 5]), 1.5811388300841898);

Population standard deviation

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([1, 2, 3, 4, 5], 0), 1.4142135623730951);

Matrix standard deviation along rows (default dim=0, flag=1)

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([[1, 2], [3, 4]]), [[0.7071067811865476, 0.7071067811865476]]);

Parameters

Input array or matrix

flag: 0 | 1

Normalization type (0: population, 1: sample). Default is 1

optional
dim: 0 | 1

Dimension to operate on (0: rows, 1: columns). Default is 0

Return Type

Computed standard deviation values

Throws

When input is invalid

std(
x: array,
flag: 0 | 1,
dim: 0 | 1,
): number

Computes the standard deviation.

Computes the standard deviation (square root of variance) for arrays or matrices. Supports both population (N) and sample (N-1) normalizations.

Examples

Sample standard deviation

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([1, 2, 3, 4, 5]), 1.5811388300841898);

Population standard deviation

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([1, 2, 3, 4, 5], 0), 1.4142135623730951);

Matrix standard deviation along rows (default dim=0, flag=1)

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([[1, 2], [3, 4]]), [[0.7071067811865476, 0.7071067811865476]]);

Parameters

x: array

Input array or matrix

flag: 0 | 1

Normalization type (0: population, 1: sample). Default is 1

dim: 0 | 1

Dimension to operate on (0: rows, 1: columns). Default is 0

Return Type

number

Computed standard deviation values

Throws

When input is invalid

std(
x: matrix,
flag: 0 | 1,
dim: 0 | 1,
): matrix

Computes the standard deviation.

Computes the standard deviation (square root of variance) for arrays or matrices. Supports both population (N) and sample (N-1) normalizations.

Examples

Sample standard deviation

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([1, 2, 3, 4, 5]), 1.5811388300841898);

Population standard deviation

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([1, 2, 3, 4, 5], 0), 1.4142135623730951);

Matrix standard deviation along rows (default dim=0, flag=1)

import { assertEquals } from "jsr:@std/assert";

assertEquals(std([[1, 2], [3, 4]]), [[0.7071067811865476, 0.7071067811865476]]);

Parameters

Input array or matrix

flag: 0 | 1

Normalization type (0: population, 1: sample). Default is 1

dim: 0 | 1

Dimension to operate on (0: rows, 1: columns). Default is 0

Return Type

Computed standard deviation values

Throws

When input is invalid

Usage

import std from "stats/std.ts";