The package "nparLD" is designed to perform nonparametric
analysis of longitudinal data in factorial experiments.
Longitudinal data are those which are collected from the same
subjects over time, and they frequently arise in biological
sciences. Nonparametric methods do not require distributional
assumptions, and are applicable to a variety of data types
(continuous, discrete, purely ordinal, and dichotomous).  Such
methods are also robust with respect to outliers and for small
sample sizes.

Kimihiro Noguchi <kinoguchi@ucdavis.edu>, Mahbub Latif
<mlatif@gwdg.de>, Karthinathan Thangavelu <kthanga@gwdg.de>,
Frank Konietschke <fkoniet@gwdg.de>, Yulia R. Gel
<ygl@math.uwaterloo.ca>, Edgar Brunner
<brunner@ams.med.uni-goettingen.de>
Performs nonparametric
analysis of longitudinal data in factorial experiments.
Longitudinal data are those which are collected from the same
subjects over time, and they frequently arise in biological
sciences. Nonparametric methods do not require distributional
assumptions, and are applicable to a variety of data types
(continuous, discrete, purely ordinal, and dichotomous).  Such
methods are also robust with respect to outliers and for small
sample sizes.

Kimihiro Noguchi <noguchk@wwu.edu>, Mahbub Latif
<mlatif@isrt.ac.bd>, Karthinathan Thangavelu,
Frank Konietschke <frank.konietschke@charite.de>, Yulia R. Gel
<yxg142030@utdallas.edu>, Edgar Brunner
<ebrunne1@gwdg.de>

Nonparametric Analysis of Multivariate Data in Factorial Designs
matrixStats, matrixcalc, MASS, gtools, Formula, methods, stats
Analysis of multivariate data with two-way completely randomized
factorial design. The analysis is based on fully nonparametric, rank-based
methods and uses test statistics based on the Dempster's ANOVA, Wilk's Lambda,
Lawley-Hotelling and Bartlett-Nanda-Pillai criteria. The multivariate
response is allowed to be ordinal, quantitative, binary or a mixture of the
different variable types. The package offers two functions performing the
analysis, one for small and the other for large sample sizes. The underlying methodology
is largely described in Bathke and Harrar (2016) <doi:10.1007/978-3-319-39065-9_7> and in
Munzel and Brunner (2000) <doi:10.1016/S0378-3758(99)00212-8>.