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, the Birnbaum 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>.

Tools for constructing, labeling, and visualizing Causal Assignment Trees
    (CATs) in settings with staggered adoption. Supports Callaway and Sant'Anna
    difference-in-differences (CSDiD) and doubly robust difference-in-difference-differences
    (DR-DDD) designs. The package helps clarify treatment timing, never-treated vs.
    not-yet-treated composition, and subgroup structure, and produces publication-quality
    diagrams and summary tables. Current functionality focuses on data-to-node mapping,
    node counts, cohort-year summaries, and high-quality tree plots suitable for empirical
    applications prior to estimation.
    Methods are based on Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001>,
    Sant'Anna and Zhao (2020) <doi:10.1016/j.jeconom.2020.06.003>, and
    Kilanko (2026) <https://github.com/VictorKilanko/catviz>.

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>.