probability prediction trees are supported. Data from genome-wide
association studies can be analyzed efficiently. In addition to data
frames, datasets of class 'gwaa.data' (R package GenABEL) can be directly

Description: A fast implementation of Random Forests, particularly suited for high dimensional data.
Ensembles of classification, regression, survival and probability prediction trees are
supported. Data from genome-wide association studies can be analyzed efficiently. In addition to
data frames, datasets of class 'gwaa.data' (R package GenABEL) can be directly analyzed.

Description: A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles
of classification, regression, survival and probability prediction trees are supported. Data from
genome-wide association studies can be analyzed efficiently. In addition to data frames, datasets
of class 'gwaa.data' (R package 'GenABEL') can be directly analyzed.

Description: A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of
classification, regression, survival and probability prediction trees are supported. Data from
genome-wide association studies can be analyzed efficiently. In addition to data frames, datasets of
class 'gwaa.data' (R package 'GenABEL') can be directly analyzed.

Description: A fast implementation of Random Forests, particularly suited for high dimensional
data. Ensembles of classification, regression, survival and probability prediction
trees are supported. Data from genome-wide association studies can be analyzed
efficiently. In addition to data frames, datasets of class 'gwaa.data' (R package

Title: Rank-Based Tests for General Factorial Designs
Author: Frank Konietschke, Sarah Friedrich, Edgar Brunner, Markus Pauly
Description: The rankFD() function calculates the Wald-type statistic (WTS) and the ANOVA-type
statistic (ATS) for nonparametric factorial designs, e.g., for count, ordinal or score data
in a crossed design with an arbitrary number of factors.