Adaptive Rejection Sampling, Original version.

A method for quantifying resilience after a stress event. A set of functions
    calculate the area of resilience that is created by the departure of baseline 
    'y' (i.e., robustness) and the time taken 'x' to return to baseline 
    (i.e., rapidity) after a stress event using the Cartesian coordinates of the 
    data. This package has the capability to calculate areas of resilience,
    growth, and cases in which resilience is not achieved 
    (e.g., diminished performance without return to baseline).

An Arsenal of 'R' functions for large-scale statistical summaries,
  which are streamlined to work within the latest reporting tools in 'R' and
  'RStudio' and which use formulas and versatile summary statistics for summary
  tables and models. The primary functions include tableby(), a Table-1-like
  summary of multiple variable types 'by' the levels of one or more categorical
  variables; paired(), a Table-1-like summary of multiple variable types paired across
  two time points; modelsum(), which performs simple model fits on one or more endpoints
  for many variables (univariate or adjusted for covariates);
  freqlist(), a powerful frequency table across many categorical variables;
  comparedf(), a function for comparing data.frames; and
  write2(), a function to output tables to a document.

An implementation of the Aligned Rank Transform technique for
    factorial analysis (see references below for details) including models with
    missing terms (unsaturated factorial models). The function first
    computes a separate aligned ranked response variable for each effect of the
    user-specified model, and then runs a classic ANOVA on each of the aligned
    ranked responses. For further details, see Higgins, J. J. and Tashtoush, S.
    (1994). An aligned rank transform test for interaction. Nonlinear World 1
    (2), pp. 201-211. Wobbrock, J.O., Findlater, L., Gergle, D. and
    Higgins,J.J. (2011). The Aligned Rank Transform for nonparametric factorial
    analyses using only ANOVA procedures. Proceedings of the ACM Conference on
    Human Factors in Computing Systems (CHI '11). New York: ACM Press, pp.
    143-146. <doi:10.1145/1978942.1978963>.