Title: Performing computations related to group sequential designs.
Description: The computations are done via the alpha spending approach
i.e. interim analyses need not to be equally spaced, and their
number need not to be specified in advance.
Packaged: Tue Feb 24 09:28:02 2009; hornik

Title: A GUI-based program to compute probabilities regarding group
Description: The package provides a Tcl/Tk-based graphical user interface. It
enables the construction of group sequential designs based on normally
distributed test statistics, and particularly to compute critical
boundaries, power, drift, and confidence intervals for such designs. All
computations are done via the alpha spending approach by Lan-DeMets.
Various alpha spending functions are made available to the user for this

Title: A GUI-Based Program to Compute Probabilities Regarding Group
Description: A graphical user interface to compute group sequential designs
based on normally distributed test statistics, particularly critical
boundaries, power, drift, and confidence intervals of such designs. All
computations are based on the alpha spending approach by Lan-DeMets with
various alpha spending functions being available to choose among.

email = "roman.pahl@gmail.com", role = c("aut", "cre"))

Packaged: Sun May 21 19:23:59 2006; soLidas

Title: Group Sequential Design Probabilities - With Graphical User
Description: 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.
Imports: tcltk, tcltk2, mvtnorm, container (>= 1.0.0)
Suggests: knitr, tinytest, rmarkdown, ggplot2, gridExtra

Title: Multiple Testing Procedure for Grouped Hypotheses
Description: 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.

Authors@R: person("Hunziker", "Alex", role = c("aut", "cre"), email = "alexhunziker@sunrise.ch")
Description: 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.