Nonparametric methods for analysis of covariance (ANCOVA) are
    distribution-free and provide a flexible statistical framework for
    situations where the assumptions of parametric ANCOVA are violated
    or when the response variable is ordinal. This package implements
    several well-known nonparametric ANCOVA procedures, including Quade,
    Puri and Sen, McSweeney and Porter, Burnett and Barr, Hettmansperger
    and McKean, Shirley, and Puri-Sen-Harwell-Serlin. The package provides
    user-friendly functions to apply these methods in practice.
	These methods are described in Olejnik et al. (1985) 
	<doi:10.1177/0193841X8500900104> and Harwell et al. (1988) 
	<doi:10.1037/0033-2909.104.2.268>.

Computes interdaily stability (IS), intradaily variability (IV) & the relative amplitude (RA) from actigraphy data as described in Blume et al. (2016) <doi: 10.1016/j.mex.2016.05.006> and van Someren et al. (1999) <doi: 10.3109/07420529908998724>. Additionally, it also computes L5 (i.e. the 5 hours with lowest average actigraphy amplitude) and M10 (the 10 hours with highest average amplitude) as well as the respective start times. The flex versions will also compute the L-value for a user-defined number of minutes. IS describes the strength of coupling of a rhythm to supposedly stable zeitgebers. It varies between 0 (Gaussian Noise) and 1 for perfect IS. IV describes the fragmentation of a rhythm, i.e. the frequency and extent of transitions between rest and activity. It is near 0 for a perfect sine wave, about 2 for Gaussian noise and may be even higher when a definite ultradian period of about 2 hrs is present. RA is the relative amplitude of a rhythm. Note that to obtain reliable results, actigraphy data should cover a reasonable number of days.

With this package, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes simultaneous p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. 2 sample comparisons can be performed with the same methods described above. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers. See Konietschke et al. (2015) <doi:10.18637/jss.v064.i09> for details.