🧮〰️ Implement Robust Weighted Least Squares Regression-based Fourier transform

[MothNik]

Now, with the Hermite-functions and a CI properly set up, the core functionality of the package can be implemented:

The Robust Weighted Least Squares approach to Fourier Transform as described in

Mihály Dobróka, Hajnalka Szegedi and Péter Vass: Inversion-Based Fourier Transform as a New Tool for Noise Rejection, 2017, DOI

[MothNik]

✔️ Checklist of things to do

• (1) implement auxiliary functions that help converting between Discrete and Continuous Fourier Transform
• (2) test auxiliary Fourier functions
• (3) add center to the Hermite functions in order to shift them
• (4) test center functionality
• (5) update function documentation, examples, and README to mention the new center functionality
• (6) implement fast approximations of important Hermite function points, namley the
  • (6.1) the position of the outermost root,
  • (6.2) the position and value of the outermost extreme point, and
  • (6.3) the point of numerical vanishing in the outermost tails
• [] (7) implement functions to compute the Discrete/Continuous Fourier Transform and or its Inverse for a Hermite function
• [] (8) test Hermite function Fourier transforms
• (9) implement object-oriented interface to Hermite functions and their Fourier transforms
• (10) implement Least Squares fit for the Fourier transform with robust weighting scheme (currently static order and alpha)

[MothNik]

A PR to develop will be made now that the first 6 points on the checklist are done.
It's a good place to make a PR to keep it "small" and also self-consistent in terms of topic covered.