aggregatejs

A comprehensive set of statistical and mathematical aggregation functions written in TypeScript.

Installation

$ npm install aggregatejs

Example

TypeScript / ES6

import { max, min, mode, quartiles } from 'aggregatejs';
// top-level exports can be imported individually (recommended)
import percentile from 'aggregatejs/dist/percentile';
import average from 'aggregatejs/dist/average';

max([100, -100, 150, -50, 250, 100]);
// => 250

min([100, -100, 150, -50, 250, 100]);
// => -100

mode([1, 2, 2, 3, 4]);
// => 2

quartiles([1, 2, 3, 4, 5, 6, 7, 8, 9]);
// => { q1: 3, q2: 5, q3: 7 }

CommonJS

const { max, min, mode, quartiles } = require('aggregatejs');
const percentile = require('aggregatejs/dist/percentile').default;
const average = require('aggregatejs/dist/average').default;

max([100, -100, 150, -50, 250, 100]);
// => 250

min([100, -100, 150, -50, 250, 100]);
// => -100

API

All aggregate functions expect an array of numbers and return computed values. All functions throw errors for invalid input (empty arrays, NaN, Infinity).

Basic Statistics

average

Returns the arithmetic mean of the numbers in array.

average([100, -100, 150, -50, 100, 250]);
// => 75

Throws:

• RangeError if array is empty
• TypeError if array contains non-numeric or non-finite values

count

Counts the numbers in array.

count([100, -100, 150, -50, 100, 250]);
// => 6

Throws:

• RangeError if array is empty
• TypeError if array contains non-numeric or non-finite values

max

Returns the largest number in array.

max([100, -100, 150, -50, 250, 100]);
// => 250

Throws:

• RangeError if array is empty
• TypeError if array contains non-numeric or non-finite values

min

Returns the smallest number in array.

min([100, -100, 150, -50, 250, 100]);
// => -100

Throws:

• RangeError if array is empty
• TypeError if array contains non-numeric or non-finite values

sum

Returns the sum of all numbers in array.

sum([100, -100, 150, -50, 100, 250]);
// => 450

Throws:

• RangeError if array is empty
• TypeError if array contains non-numeric or non-finite values

range

Returns the difference between the maximum and minimum values in array.

range([100, -100, 150, -50, 250, 100]);
// => 350 (250 - (-100))

Throws:

• RangeError if array is empty
• TypeError if array contains non-numeric or non-finite values

mode

Returns the most frequently occurring value(s) in array. If multiple values have the same highest frequency, returns an array of all modes.

mode([1, 2, 2, 3, 4]);
// => 2

mode([1, 1, 2, 2, 3]);
// => [1, 2]

Throws:

• RangeError if array is empty
• TypeError if array contains non-numeric or non-finite values

Positional Statistics

median

Returns the median (middle value) of the numbers in array. Does not mutate the input array.

median([100, -100, 150, -50, 100, 250]);
// => 100

Throws:

• RangeError if array is empty
• TypeError if array contains non-numeric or non-finite values

percentile

Returns the k-th percentile of values in array, where k is between 0 and 1. Does not mutate the input array.

percentile([100, -100, 150, -50, 100, 250], 0.25);
// => -12.5

percentile([100, -100, 150, -50, 100, 250], 0.50);
// => 100

percentile([100, -100, 150, -50, 100, 250], 0.95);
// => 225

Throws:

• RangeError if array is empty or k is outside [0, 1] range
• TypeError if array contains non-numeric or non-finite values, or k is not a finite number

quartiles

Returns an object containing the first quartile (Q1), median (Q2), and third quartile (Q3) of the numbers in array.

quartiles([1, 2, 3, 4, 5, 6, 7, 8, 9]);
// => { q1: 3, q2: 5, q3: 7 }

Throws:

• RangeError if array is empty
• TypeError if array contains non-numeric or non-finite values

Dispersion Statistics

variance

Returns the population variance of the numbers in array.

variance([2, 4, 4, 4, 5, 5, 7, 9]);
// => 4

Throws:

• RangeError if array is empty
• TypeError if array contains non-numeric or non-finite values

deviation

Returns the standard deviation of the numbers in array.

deviation([2, 4, 4, 4, 5, 5, 7, 9]);
// => 2

Throws:

• RangeError if array is empty
• TypeError if array contains non-numeric or non-finite values

Correlation and Covariance

correlation

Returns the Pearson correlation coefficient between two arrays of numbers. The correlation measures the strength and direction of a linear relationship between two variables, ranging from -1 (perfect negative correlation) to 1 (perfect positive correlation). A value of 0 indicates no linear correlation.

correlation([1, 2, 3, 4, 5], [2, 4, 6, 8, 10]);
// => 1 (perfect positive correlation)

correlation([1, 2, 3, 4, 5], [5, 4, 3, 2, 1]);
// => -1 (perfect negative correlation)

correlation([1, 2, 3, 4, 5], [1, 3, 2, 5, 4]);
// => 0.1 (weak correlation)

Throws:

• RangeError if arrays are empty, have different lengths, or contain only constant values (zero standard deviation)
• TypeError if arrays contain non-numeric or non-finite values

covariance

Returns the population covariance between two arrays of numbers. Covariance measures how two variables change together. Positive values indicate that both variables tend to increase together, while negative values indicate an inverse relationship.

covariance([1, 2, 3, 4, 5], [2, 4, 6, 8, 10]);
// => 4

covariance([1, 2, 3], [3, 2, 1]);
// => -1

Throws:

• RangeError if arrays are empty or have different lengths
• TypeError if arrays contain non-numeric or non-finite values

Specialized Means

geometricMean

Returns the geometric mean of the numbers in array. The geometric mean is the nth root of the product of n numbers.

geometricMean([2, 8]);
// => 4

geometricMean([1, 3, 9, 27, 81]);
// => 9

Throws:

• RangeError if array is empty or contains negative numbers
• TypeError if array contains non-numeric or non-finite values

Note: Returns 0 if any value in the array is 0.

harmonicMean

Returns the harmonic mean of the numbers in array. The harmonic mean is the reciprocal of the arithmetic mean of reciprocals.

harmonicMean([1, 2, 4]);
// => 1.7142857142857142

harmonicMean([2, 3]);
// => 2.4

Throws:

• RangeError if array is empty or contains zero
• TypeError if array contains non-numeric or non-finite values

Error Handling

All functions in v1.0.0+ provide robust error handling:

• Empty Arrays: All functions throw RangeError with message "Array cannot be empty"
• Invalid Input: All functions throw TypeError with descriptive messages for:
  • Non-array inputs
  • Arrays containing NaN
  • Arrays containing Infinity or -Infinity
  • Non-numeric values
• Invalid Parameters: Functions like percentile() validate their parameters and throw appropriate errors

Example Error Handling

import { max } from 'aggregatejs';

try {
  max([]);
} catch (error) {
  console.error(error); // RangeError: Array cannot be empty
}

try {
  max([1, NaN, 3]);
} catch (error) {
  console.error(error); // TypeError: All array elements must be finite numbers
}

Migration from v0.x to v1.0.0

See CHANGELOG.md for detailed migration instructions.

Key Breaking Changes:

• Empty arrays now throw errors instead of returning 0
• Invalid input (NaN, Infinity) now throws errors
• median() and percentile() no longer mutate input arrays

Development

Building

$ npm run build

Running tests

$ npm test

Running performance benchmarks

Performance tests help measure and track the execution speed of aggregation functions across different dataset sizes.

# Run all performance benchmarks
$ npm run perf

# Run specific benchmark suites
$ npm run perf:basic          # Basic statistics (average, sum, min, max, count, range)
$ npm run perf:positional     # Positional statistics (median, percentile, quartiles)
$ npm run perf:specialized    # Specialized functions (mode, variance, deviation, geometric/harmonic means)

Performance Test Coverage:

• Small datasets (10 elements) - Typical use cases
• Medium datasets (1,000 elements) - Common real-world scenarios
• Large datasets (100,000 elements) - Stress testing
• Extra large datasets (1,000,000 elements) - Extreme performance testing

The benchmarks provide:

• Operations per second (ops/sec)
• Mean execution time
• Margin of error
• Sample size for statistical significance

Clean build artifacts

$ npm run clean

License

MIT