Forward Mode Automatic Differentiation for Julia
Syntax highlighting and other enhancements for the Julia REPL
Statically sized arrays for Julia
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms
Fortran-like arrays with arbitrary, zero or negative starting indices.
Explicit SIMD vector operations for Julia
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc.
Line search methods for optimization and root-finding
Designs for new Base array interface primitives, used widely through scientific machine learning (SciML) and other organizations
Ask the CPU for cache sizes, SIMD feature support, a running hypervisor, and more.
Calculate with error-free, faithful, and compensated transforms and extended signficands.
Transformations to contrained variables from ℝⁿ.
Utility functions for exponential integrators for the SciML scientific machine learning ecosystem
Positive-definite "approximations" to matrices
Efficient filtering and linear algebra routines for multidimensional arrays