For a sequence of event occurence times, we are interested in
finding subsequences in it that are too "regular". We define regular as being
significantly different from a homogeneous Poisson process. The departure from
the Poisson process is measured using a L1 distance. See Di and Perlman 2007
for more details.

Functions for unary and binary regression tests
GPL2-QA (like GPL2 but requires users to participate in Quality Assurance aka Regression Testing, details to be defined)
Tue Aug 21 12:53:01 2007; Jens Oehlschlaegel

Mon Oct 22 20:40:23 2007; Jens Oehlschlaegel

Thu Jan 29 19:02:11 2009; Jens Oehlschlaegel

formattable, effsize, mefa, plyr, plotrix, sjPlot, sjmisc,
sjlabelled,
c(person("Daniela", "Recchia", role = c("aut","cre"), email = "daniela.rodriguesrecchia@uni-wh.de"), person("Thomas", "Ostermann", role = "ctb") )
In repeated measures studies with extreme large or small values it is common
that the subjects measurements on average are closer to the mean of the basic population.
Interpreting possible changes in the mean in such situations can lead to biased results
since the values were not randomly selected, they come from truncated sampling.
This method allows to estimate the range of means where treatment effects are likely to occur
when regression toward the mean is present.
Ostermann, T., Willich, Stefan N. & Luedtke, Rainer. (2008). Regression toward the mean - a detection method for unknown population mean based on Mee and Chua's algorithm. BMC Medical Research Methodology.<doi:10.1186/1471-2288-8-52>.
Daniela Recchia [aut, cre],
Thomas Ostermann [ctb]

Tools for linear, nonlinear and nonparametric regression
and classification.  Parametric fit assessment using
nonparametric methods. One vs. All and All vs. All
multiclass classification.  Nonparametric regression for
general dimension, locally-linear option.  Nonlinear
regression with Eickert-White method for dealing with
heteroscedasticity, k-NN for general dimension and
general descriptive functions.

Tools for linear, nonlinear and nonparametric regression
and classification.  Novel graphical methods for assessment
of parametric models using nonparametric methods. One
vs. All and All vs. All multiclass classification, optional
class probabilities adjustment.  Nonparametric regression
(k-NN) for general dimension, local-linear option.  Nonlinear
regression with Eickert-White method for dealing with
heteroscedasticity.  Utilities for converting time series
to rectangular form.  Utilities for conversion between
factors and indicator variables.  Some code related to
"Statistical Regression and Classification: from Linear
Models to Machine Learning", N. Matloff, 2017, CRC,
ISBN 9781498710916.