A Julia package to construct orthogonal polynomials, their quadrature rules, and use it with polynomial chaos expansions.

Nodes and modes for high order finite element methods

AlgDiff is a Python class implementing all necessary tools for the design, analysis, and discretization of algebraic differentiators. An interface to Matlab is also provided.

Associated Legendre Polynomials and Spherical Harmonics in Julia

A Python module to compute multidimensional arrays of evaluated (orthogonal) functions.

Generate orthogonal polynomials for arbitrary probability density functions

# Universal Closed-Form Hypergeometric Representation of SU(2) 3nj Symbols

# SU(2) Node Matrix Elements

# Closed-Form Finite Recurrences for SU(2) 3nj Symbols

Closed-form hypergeometric product formula and reproducible implementations for SU(2) 3nj recoupling coefficients. Includes reference datasets, cross-repo validation harness, and paper sources.

Raku package for functionalities based on Chebyshev polynomials.

Rootfinders based on colleague matrices

A Julia package for working with discrete orthogonal polynomial ensembles and determinantal point processes.

Orthogonal polynomials in 3D, based on a tensor product construction of 1D orthogonal polynomials. The 1D polynomials are defined in terms of a three-term recurrence relation derived with Gram-Schmidt on standard monomials.

quad-precision orthogonal polynomial least squares

Orthogonal regression polynomial approximation: no SLE, fast, high precision, no dependencies

Jacobi, Gegenbauer, Chebyshev of first, second, third, fourth kind, Legendre, Laguerre, Hermite, shifted Chebyshev and Legendre polynomials

All my assignments to the course MM5016 at Stockholm University