Provides an implementation of a kernel-embedding of probability test for elliptical distribution. This is an asymptotic test for elliptical distribution under general alternatives, and the location and shape parameters are assumed to be unknown. Some side-products are posted, including the transformation between rectangular and polar coordinates and two product-type kernel functions. See Tang and Li (2024) <doi:10.48550/arXiv.2306.10594> for details.

Implements the kernel method of test equating as defined in von Davier, A. A., Holland, P. W. and Thayer, D. T. (2004) <doi:10.1007/b97446> and Andersson, B. and Wiberg, M. (2017) <doi:10.1007/s11336-016-9528-7> using the CB, EG, SG, NEAT CE/PSE and NEC designs, supporting Gaussian, logistic and uniform kernels and unsmoothed and pre-smoothed input data.

Interface to 'Keras' <https://keras.io>, a high-level neural
  networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation,
  supports both convolution based networks and recurrent networks (as well as
  combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.

Interface to 'Keras' <https://keras.io>, a high-level neural
  networks API. 'Keras' was developed with a focus on enabling fast experimentation,
  supports both convolution based networks and recurrent networks (as well as
  combinations of the two), and runs seamlessly on both CPU and GPU devices.

'Keras Tuner' <https://keras-team.github.io/keras-tuner/> is a hypertuning framework made for humans. 
             It aims at making the life of AI practitioners, hypertuner 
             algorithm creators and model designers as simple as possible by 
             providing them with a clean and easy to use API for hypertuning. 
             'Keras Tuner' makes moving from a base model to a hypertuned one quick and 
             easy by only requiring you to change a few lines of code.

A new practical method to evaluate whether relationships between two sets of high-dimensional variables are different or not across two conditions.
    Song, H. and Wu, M.C. (2023) 
    <arXiv:2307.15268>.