Group Sequential Design Probabilities - With Graphical User
Interface
Computes probabilities related to group sequential designs for
normally distributed test statistics. Enables to derive critical
boundaries, power, drift, and confidence intervals of such designs.
Supports the alpha spending approach by Lan-DeMets.
tcltk, tcltk2, mvtnorm, container (>= 1.0.0)
knitr, tinytest, rmarkdown, ggplot2, gridExtra

Multiple Testing Procedure for Grouped Hypotheses
Contains functions for a two-stage multiple testing procedure for grouped hypothesis, aiming at controlling both the total posterior false discovery rate and within-group false discovery rate.

person("Hunziker", "Alex", role = c("aut", "cre"), email = "alexhunziker@sunrise.ch")
A growing self-organizing map (GrowingSOM, GSOM) is a growing variant of the popular self-organizing map (SOM).
A growing self-organizing map is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a
two-dimensional representation of the input space of the training samples, called a map.

A growing self-organizing map (GrowingSOM, GSOM) is a growing variant of the popular self-organizing map (SOM).
A growing self-organizing map is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a
low-dimensional representation of the input space of the training samples, called a map.

Patterns and Statistical Differences Between Two Groups of
Strings
Methods include converting series of event names to strings,
discovering common patterns in a group of strings, discovering "unique" patterns
when comparing two groups of strings as well as the number and starting position
of each "unique" pattern in each string, finding the transition information, and
statistically comparing the difference between two groups of strings.

Methods include converting series of event names to strings,
discovering common patterns in a group of strings, discovering "unique" patterns
when comparing two groups of strings as well as the number and starting position
of each "unique" pattern in each string, finding the transition matrix and information, and
statistically comparing the difference between two groups of strings.