Base ndarray statistical functions.
JavaScript
0
0
Apache License 2.0
This is a JavaScript library that provides base statistical functions for ndarray objects. It's part of the stdlib project, which aims to enable numerical computation in web browsers and Node.js. The library includes functions for calculating statistics like mean, max, min, standard deviation, and covariance on one-dimensional ndarrays, with support for different data types (double-precision, single-precision) and various algorithms (Welford's, pairwise summation, etc.). It's designed for developers working on numerical and scientific computing applications in JavaScript.
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Support the dependencies of stdlib-js/stats-base-ndarray
Compute the covariance of two one-dimensional ndarrays provided known means and using a one-pass textbook algorithm.
Compute the cumulative maximum value of a one-dimensional ndarray.
Compute the cumulative minimum value of a one-dimensional ndarray.
Compute the covariance of two one-dimensional double-precision floating-point ndarrays provided known means and using a one-pass textbook algorithm.
Compute the cumulative maximum value of a one-dimensional double-precision floating-point ndarray.
Compute the cumulative maximum absolute value of a one-dimensional double-precision floating-point ndarray.
Compute the cumulative minimum value of a one-dimensional double-precision floating-point ndarray.
Compute the cumulative minimum absolute value of a one-dimensional double-precision floating-point ndarray.
Compute the maximum value of a one-dimensional double-precision floating-point ndarray.
Compute the maximum absolute value of a one-dimensional double-precision floating-point ndarray.
Compute the maximum absolute value of a sorted one-dimensional double-precision floating-point ndarray.
Compute the maximum value of a sorted one-dimensional double-precision floating-point ndarray.
Compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray.
Compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using an improved Kahan–Babuška algorithm.
Compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using a second-order iterative Kahan–Babuška algorithm.
Compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm.
Compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm with pairwise summation.
Compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using ordinary recursive summation.
Compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using a two-pass error correction algorithm.
Compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using pairwise summation.
Compute the mean and standard deviation of a one-dimensional double-precision floating-point ndarray.
Compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using Welford's algorithm.
Compute the median value of a sorted one-dimensional double-precision floating-point ndarray.
Compute the mid-range of a one-dimensional double-precision floating-point ndarray.
Compute the minimum value of a one-dimensional double-precision floating-point ndarray.
Compute the minimum absolute value of a one-dimensional double-precision floating-point ndarray.
Compute the minimum value of a sorted one-dimensional double-precision floating-point ndarray.
Calculate the maximum value of a one-dimensional double-precision floating-point ndarray according to a mask.
Calculate the minimum value of a one-dimensional double-precision floating-point ndarray according to a mask.
Calculate the range of a one-dimensional double-precision floating-point ndarray according to a mask.
Compute the maximum value of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values.
Compute the maximum absolute value of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values.
Compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values.
Compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using ordinary recursive summation.
Compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using a two-pass error correction algorithm.
Compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using pairwise summation.
Compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using Welford's algorithm.
Compute the mid-range of a one-dimensional double-precision floating-point ndarray, ignoring NaN values.
Compute the minimum value of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values.
Compute the minimum value of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values.
Compute the maximum value of a double-precision floating-point ndarray according to a mask, ignoring `NaN` values.
Compute the minimum value of a double-precision floating-point ndarray according to a mask, ignoring `NaN` values.
Calculate the range of a one-dimensional double-precision floating-point ndarray according to a mask, ignoring NaN values.
Compute the range of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values.
Compute the range of a one-dimensional double-precision floating-point ndarray.
Compute the range of absolute values of a one-dimensional double-precision floating-point ndarray.
Compute the standard deviation of a one-dimensional double-precision floating-point ndarray.
Compute the standard deviation of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm.
Compute the standard deviation of a one-dimensional double-precision floating-point ndarray using a two-pass algorithm.
Compute the standard deviation of a one-dimensional double-precision floating-point ndarray using a one-pass textbook algorithm.
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