Miscellaneous functions and data used in psychological research and teaching. Keng 
    currently has a built-in dataset depress, and could (1) scale a vector; 
    (2) divide a vector into three groups, 
    (3) compute the cut-off values of Pearson's r with known sample size; 
    (4) test the significance and compute the post-hoc power for Pearson's r with known sample size; 
    (5) conduct a priori power analysis and plan the sample size for Pearson's r; 
    (6) compare lm()'s fitted outputs using R-squared, f_squared, post-hoc power, 
    and PRE (Proportional Reduction in Error, also called partial R-squared or 
    partial Eta-squared); (7) calculate PRE from partial correlation, Cohen's f, or f_squared; 
    (8) conduct a priori power analysis and plan the sample size for one or a set of predictors in 
    regression analysis; (9) conduct post-hoc power analysis for one or a set of predictors in 
    regression analysis with known sample size; (10) randomly pick numbers for Chinese Super Lotto
    and Double Color Balls; (11) assess course objective achievement in Outcome-Based Education.

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.