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

Uniform asymptotic forms for SU(2) 3nj symbols in large quantum number limits. Mathematical analysis of recoupling coefficients and asymptotic expansions for quantum angular momentum theory.

Finite closed-form recurrence relations and seed data for SU(2) 3nj recoupling symbols

Closed-form SU(2) operator matrix elements for arbitrary-valence nodes via a universal generating functional and hypergeometric expansion.

Raku package for functionalities based on Chebyshev polynomials.

Closed-form expressions for SU(2) 3nj symbols using recoupling theory. Mathematical framework for quantum angular momentum coupling coefficients and spin network calculations.

quad-precision orthogonal polynomial least squares

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.

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

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

All my assignments to the course MM5016 at Stockholm University