A comprehensive toolkit for generating continuous test norms in 
     psychometrics and biometrics, and analyzing model fit. The package offers 
     both distribution-free modeling using Taylor polynomials and parametric 
     modeling using the beta-binomial and the 'Sinh-Arcsinh' distribution. 
     Originally developed for achievement tests, it is applicable to a wide 
     range of mental, physical, or other test scores dependent on continuous or 
     discrete explanatory variables. The package provides several advantages: 
     It minimizes deviations from representativeness in subsamples, interpolates 
     between discrete levels of explanatory variables, and significantly reduces 
     the required sample size compared to conventional norming per age group. 
     cNORM enables graphical and analytical evaluation of model fit, 
     accommodates a wide range of scales including those with negative and 
     descending values, and even supports conventional norming. It generates 
     norm tables including confidence intervals. It also includes methods for 
     addressing representativeness issues through Iterative Proportional Fitting. 
     Based on Lenhard et al. (2016) 
    <doi:10.1177/1073191116656437>, Lenhard et al. (2019) 
    <doi:10.1371/journal.pone.0222279>, Lenhard and Lenhard (2021) 
    <doi:10.1177/0013164420928457> and Gary et al. (2023) 
    <doi:10.1007/s00181-023-02456-0>.

DNA copy number data evaluation using both their initial form 
    (copy number as a noisy function of genomic position) and their 
    approximation by a piecewise-constant function (segmentation), 
    for the purpose of identifying genomic regions where the copy number 
    differs from the norm.

We unify various nonparametric hypothesis testing problems in a framework of permutation testing, enabling hypothesis testing on multi-sample, multidimensional data and contingency tables. Most of the functions available in the R environment to implement permutation tests are single functions constructed for specific test problems; to facilitate the use of the package, the package encapsulates similar tests in a categorized manner, greatly improving ease of use.
    We will all provide functions for self-selected permutation scoring methods and self-selected p-value calculation methods (asymptotic, exact, and sampling). For two-sample tests, we will provide mean tests and estimate drift sizes; we will provide tests on variance; we will provide paired-sample tests; we will provide correlation coefficient tests under three measures. For multi-sample problems, we will provide both ordinary and ordered alternative test problems. For multidimensional data, we will implement multivariate means (including ordered alternatives) and multivariate pairwise tests based on four statistics; the components with significant differences are also calculated. For contingency tables, we will perform permutation chi-square test or ordered alternative.