blas-ext-base-ndarray-dlast-index-of
Return the last index of a specified search element in a one-dimensional double-precision floating-point ndarray.
stats-strided-dsemtk
Calculate the standard error of the mean for a double-precision floating-point strided array using a one-pass textbook algorithm.
math-array-tools-unary
Constructor for applying a unary function to each element in an input array.
stats-strided-dztest
Compute a one-sample Z-test for a double-precision floating-point strided array.
@stdlib/array-base-map5d
Apply a function to elements in a five-dimensional nested input array and assign results to elements in a new five-dimensional nested output array.
array-base-broadcasted-quaternary5d
Apply a quaternary callback to elements in four broadcasted input arrays and assign results to elements in a five-dimensional nested output array.
stats-strided-dnanmeanpn
Calculate the arithmetic mean of a double-precision floating-point strided array, ignoring NaN values and using a two-pass error correction algorithm.
stats-array-min-by
Calculate the minimum value of an array via a callback function.
@stdlib/array-base-quaternary3d
Apply a quaternary callback to elements in four three-dimensional nested input arrays and assign results to elements in a three-dimensional nested output array.
@stdlib/array-base-unarynd
Apply a unary callback to elements in an n-dimensional nested input array and assign results to elements in an n-dimensional nested output array.
array-base-unary4d-by
Apply a unary function to each element retrieved from a four-dimensional nested input array according to a callback function and assign results to elements in a four-dimensional nested output array.
@stdlib/random-array-logistic
Create an array containing pseudorandom numbers drawn from a logistic distribution.
lapack-base-spttrf
Compute the `L * D * L^T` factorization of a real symmetric positive definite tridiagonal matrix `A`.
@stdlib/random-array-arcsine
Create an array containing arcsine distributed pseudorandom numbers.
array-base-broadcasted-quaternary3d
Apply a quaternary callback to elements in four broadcasted input arrays and assign results to elements in a three-dimensional nested output array.
array-base-broadcasted-ternary5d
Apply a ternary callback to elements in three broadcasted input arrays and assign results to elements in a five-dimensional nested output array.
@stdlib/array-base-quinary4d
Apply a quinary callback to elements in five four-dimensional nested input arrays and assign results to elements in a four-dimensional nested output array.
@stdlib/blas-base-matrix-triangle-enum2str
Return the BLAS matrix triangle string associated with a BLAS matrix triangle enumeration constant.
stats-strided-dnanvarianceyc
Calculate the variance of a double-precision floating-point strided array ignoring NaN values and using a one-pass algorithm proposed by Youngs and Cramer.
@stdlib/array-base-unary2d
Apply a unary callback to elements in a two-dimensional nested input array and assign results to elements in a two-dimensional nested output array.
@stdlib/array-base-binary3d
Apply a binary callback to elements in three-dimensional nested input arrays and assign results to elements in a three-dimensional nested output array.
stats-strided-nanmax
Calculate the maximum value of a strided array, ignoring NaN values.
array-base-broadcasted-ternary4d
Apply a ternary callback to elements in three broadcasted input arrays and assign results to elements in a four-dimensional nested output array.
stats-array-variancetk
Calculate the variance of an array using a one-pass textbook algorithm.
@stdlib/ndarray-maybe-broadcast-array
Broadcast an ndarray to a specified shape if and only if the specified shape differs from the provided ndarray's shape.
ndarray-base-assert-is-integer-index-data-type
Test if an input value is a supported ndarray integer index data type.
@stdlib/array-base-mskunary3d
Apply a unary callback to elements in a three-dimensional nested input array according to elements in a three-dimensional nested mask array and assign results to elements in a three-dimensional nested output array.
stats-strided-dmeanpn
Calculate the arithmetic mean of a double-precision floating-point strided array using a two-pass error correction algorithm.
stats-array-meanwd
Calculate the arithmetic mean of an array using Welford's algorithm.
stats-strided-dnanstdevch
Calculate the standard deviation of a double-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm.
stats-array-nanmin
Calculate the minimum value of an array, ignoring NaN values.
stats-array-varianceyc
Calculate the variance of an array using a one-pass algorithm proposed by Youngs and Cramer.
stats-array-nanmax
Calculate the maximum value of an array, ignoring NaN values.
stats-array-variancewd
Calculate the variance of an array using Welford's algorithm.
stats-array-variancepn
Calculate the variance of an array using a two-pass algorithm.
stats-array-variancech
Calculate the variance of an array using a one-pass trial mean algorithm.
stats-strided-dvariancech
Calculate the variance of a double-precision floating-point strided array using a one-pass trial mean algorithm.
stats-strided-svariance
Calculate the variance of a single-precision floating-point strided array.
stats-array-mskmax
Calculate the maximum value of an array according to a mask.
blas-ext-base-wasm-dapxsumpw
Add a constant to each double-precision floating-point strided array element and compute the sum using pairwise summation.
@stdlib/array-base-unary3d
Apply a unary callback to elements in a three-dimensional nested input array and assign results to elements in a three-dimensional nested output array.
stats-strided-dsnanmeanpn
Calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values, using a two-pass error correction algorithm with extended accumulation, and returning an extended precision result.
stats-strided-nanmax-by
This repository provides a function for computing the maximum value of a strided array while ignoring NaN values. Explore the code and contribute to enhance numerical computation in JavaScript! 🐱💻📊