Computes a structural similarity metric (after the style of
    MS-SSIM for images) for binary and categorical 2D and 3D images. Can be
    based on accuracy (simple matching), Cohen's kappa, Rand index, adjusted
    Rand index, Jaccard index, Dice index, normalized mutual information, or
    adjusted mutual information. In addition, has fast computation
    of Cohen's kappa, the Rand indices, and the two mutual informations.
    Implements the methods of Thompson and Maitra (2020) <doi:10.48550/arXiv.2004.09073>.

Provides methods of computerized adaptive testing for survey researchers.  See Montgomery and Rossiter (2020) <doi:10.1093/jssam/smz027>. Includes functionality for data fit with the classic item response methods including the latent trait model, Birnbaum`s three parameter model, the graded response, and the generalized partial credit model.  Additionally, includes several ability parameter estimation and item selection routines.  During item selection, all calculations are done in compiled C++ code.

This function conducts the Cochran-Armitage trend test to a 2 by k contingency table. It will report the test statistic (Z) and p-value.A linear trend in the frequencies will be calculated, because the weights (0,1,2) will be used by default.

Provides functions for computing the one-sided p-values of the Cochran-Armitage trend test
  statistic for the asymptotic and the exact conditional test. The computation of the p-value for the exact test
  is performed using an algorithm following an idea by Mehta, et al. (1992) <doi:10.2307/1390598>.

A new robust principal component analysis algorithm is implemented that relies upon the Cauchy Distribution. The algorithm is suitable for high dimensional data even if the sample size is less than the number of variables. The methodology is described in this paper: Fayomi A., Pantazis Y., Tsagris M. and Wood A.T.A. (2024). "Cauchy robust principal component analysis with applications to high-dimensional data sets". Statistics and Computing, 34: 26. <doi:10.1007/s11222-023-10328-x>.