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.

A workflow to generate and analyze signatures based on copy number
     data using non-negative matrix factorization (NMF) in an approach similar 
     to that used in mutational signatures. It can be used to extract features 
     from Copy number segment data and use that to find a subset of copy number
     signatures which can be further used to correlate with other relevant data.
     For more on 'NMF' see Gaujoux (2013) <doi:10.1186/1471-2105-11-367>.