This JavaScript package provides a function to perform a chi-square goodness-of-fit test, which determines if observed data fits an expected distribution. It's designed for developers working with statistical analysis in JavaScript, particularly those needing to validate whether sample data matches a theoretical distribution or set of expected frequencies. The package is part of the larger stdlib project, a comprehensive numerical computing library for JavaScript and Node.js.
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Support the dependencies of stdlib-js/stats-chi2gof
Generate a linearly spaced numeric array according to a provided increment.
Float64Array.
Test if an object has a specified property.
Test if a value is a boolean.
Test if a value is a collection.
Test if a value is NaN.
Test if a value is ndarray-like.
Test if a value is a number having a nonnegative integer value.
Test if a value is a number.
Test if a value is a plain object.
Test if a value is a number having a positive integer value.
Test if a value is a string.
Multiply a vector `x` by a constant and add the result to `y`.
Multiply a double-precision floating-point vector by a constant.
Fill a double-precision floating-point strided array with a specified scalar constant.
Calculate the sum of double-precision floating-point strided array elements using pairwise summation.
Double-precision floating-point positive infinity.
Square root of double-precision floating-point epsilon.
Round a double-precision floating-point number to the nearest multiple of 10^n.
Compute the absolute difference of two real numbers.
Sample elements from an array-like object.
Bernoulli distribution probability mass function (PMF).
Binomial distribution probability mass function (PMF).
Chi-squared distribution cumulative distribution function (CDF).
Discrete uniform distribution probability mass function (PMF).
Geometric distribution probability mass function (PMF).
Hypergeometric distribution probability mass function (PMF).
Negative binomial distribution probability mass function (PMF).
Poisson distribution probability mass function (PMF).
Insert supplied variable values into a format string.
stdlib TypeScript type declarations.
Define a non-enumerable read-only accessor.
Define a non-enumerable read-only property.
Format an error message for production.
Create a zero-filled generic array.
Int32Array.
Test if an array-like value contains a search value.
Test if a value is an object.
Difference between one and the smallest value greater than one that can be represented as a double-precision floating-point number.
Compute the absolute value of a double-precision floating-point number.
Return the maximum value.
Discrete uniform distributed pseudorandom numbers.
Poisson distributed random numbers.
tap-producing test harness for node and browsers
Yet another JS code coverage tool that computes statement, line, function and branch coverage with module loader hooks to transparently add coverage when running tests. Supports all JS coverage use ca
Minimal TAP output formatter.
Benchmark harness.
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