This version of the permutational algorithm generates a
        dataset in which event and censoring times are conditional on
        an user-specified list of covariates, some or all of which are
        time-dependent.

Calculates multivariate analysis of variance based on permutations and some associated pictorial representations. The pictorial representation is based on the principal coordinates of the group means. There are some original results that will be published soon.

Performance metric provides different performance measures like mean squared error, root mean square error, mean absolute deviation, mean absolute percentage error etc. of a fitted model. These can provide a way for forecasters to quantitatively compare the performance of competing models. For method details see (i) Pankaj Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/44138>.

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>.