A simple shared suite of common derivative definitions

Flexible filtering and smoothing in Julia

Provides a modeling interface for mixed complementarity problems (MCP) and math programs with equilibrium problems (MPEC) via JuMP

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Library for multidimensional numerical integration with four independent algorithms: Vegas, Suave, Divonne, and Cuhre.

Fast, SIMD-accelerated extended-precision arithmetic for Julia

A simple hypergraphs package for the Julia programming language

An Extensible Test Matrix Collection for Julia

Create truth tables in Julia!

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

Julia bindings for the Intel Vector Math Library

Call Mathematica from Julia

Julia bindings for the Intel Vector Math Library

A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations

A package for graphs with vertex labels and metadata in Julia

A Julia package for representing infinite-dimensional arrays

A Julia package for describing domains as continuous sets of elements

Build Petri net models compositionally

A Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality

Julia Wrappers for the arpack-ng Fortran library

A Julia package for calculating hypergeometric functions

Taylor-mode automatic differentiation for higher-order derivatives

Geometry types for Julia

Calculate with error-free, faithful, and compensated transforms and extended significands.

Integrated Assessment Modeling Framework

Fast matrix multiplication and division for Toeplitz matrices in Julia

Building dynamical systems compositionally

Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

Julia package for tensors as multidimensional arrays, with functionalty within Tucker format, Kruskal (CP) format, Hierarchical Tucker format and Tensor Train format.

A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)

Full-featured traits in Julia. Without full features how dare I say this?

Groebner bases in (almost) pure Julia

Calculate rigorously the feasible region for a set of real-valued inequalities with Julia

Rigorous function approximation using Taylor models in Julia

A package for performing Singular Spectrum Analysis (SSA) and time-series decomposition

Basic numerical integration routines for presampled data.

Richardson extrapolation in Julia

Julia package for solving singular integral equations

Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions