Implements an extension of the Chacko chi-square test for
    ordered vectors (Chacko, 1966, <https://www.jstor.org/stable/25051572>).
    Our extension brings the Chacko test to the computer age by implementing
    a permutation test to offer a numeric estimate of the p-value, which is
    particularly useful when the analytic solution is not available.

Provides tools for statistical testing of correlation coefficients through robust 
    permutation method and large sample approximation method. Tailored to different types 
    of correlation coefficients including Pearson correlation coefficient, weighted 
    Pearson correlation coefficient, Spearman correlation coefficient, and Lin's 
    concordance correlation coefficient.The robust permutation test controls type I error
    under general scenarios when sample size is small and two variables are dependent but 
    uncorrelated. The large sample approximation test generally controls type I error when
    the sample size is large (>200).

An add-on to the 'party' package, with a faster implementation 
   of the partial-conditional permutation importance for random forests. The 
   standard permutation importance is implemented exactly the same as in 
   the 'party' package. The conditional permutation importance can be 
   computed faster, with an option to be backward compatible to the 'party' 
   implementation. The package is compatible with random forests fit using the 
   'party' and the 'randomForest' package. The methods are described in
   Strobl et al. (2007) <doi:10.1186/1471-2105-8-25> and 
   Debeer and Strobl (2020) <doi:10.1186/s12859-020-03622-2>.

Can be used to carry out permutation based gene expression pathway analysis. This work was supported by a National Institute of Allergy and Infectious Disease/National Institutes of Health contract (No. HHSN272200900059C).

Provides randomization using permutation for applications.  To provide a Quality Control (QC) check, QC samples can be randomized within strata.  A second function allows for the ability to ”switch” samples to meet set requirements and perform a certain amount of minimization on these switches.  The functions are flexible for users by specifying strata size and number of QC samples per strata.  The randomization meets the following requirements
    • QC sample requirements: QC samples not adjacent, QC
    samples from same mother must follow certain patterns.
    • Matched sample sets must be within a single strata, and next
    to each other.