AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system.

Julia implementation of Data structures

Julia macro for re-exporting one module from another

Mathematical functions related to statistics.

Basic statistics for Julia

A common solve function for scientific machine learning (SciML) and beyond

Reduce time-to-first-execution of Julia code

Archive of functions that emulate R's d-p-q-r functions for probability distributions

A Julia package for probability distributions and associated functions.

A Julia package for calculating hypergeometric functions

Compatibility across Julia versions

A statistics-focused namespace for packages to share functions

Extensions for Julia's docsystem.

Adaptive 1d numerical Gauss–Kronrod integration in Julia

Julia package for lazily representing matrices filled with a single entry

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Statically sized arrays for Julia

Julia package for Gaussian quadrature

Back to the stone age!

Julia implementation of associative containers that preserve insertion order

Interface for function inversion in Julia

SparseArrays.jl is a Julia stdlib

Interface for transformation functions in Julia

A simple shared suite of common derivative definitions

Fast complex polynomial root finder, with support for arbitrary precision calculations

Special mathematical functions in Julia

Project Preferences Package

`@pack!` and `@unpack` macros

Uniform Interface for positive definite matrices of various structures

Julia math built-ins which return NaN and accumulator functions which ignore NaN

Defines additional irrationals

Interface package for StaticArrays.jl

Interface for mathematical/statistical densities in Julia

Lazy code loading for Julia

Extra sorting algorithms extending Julia's sorting API

Julia package for various special functions based on `log` and `exp`.

Missing value support for Julia

Compute the nearest correlation matrix using a pure Julia implementation

An efficient sampler for discrete random variables

Compute the Pearson correlation to be used in Gaussian copulas