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.

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.

Rank-Based Tests for General Factorial Designs
Frank Konietschke, Sarah Friedrich, Edgar Brunner, Markus Pauly
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.

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.
Brunner, E., Bathke, A. and Konietschke, F. (2018) <doi:10.1007/978-3-030-02914-2>.

Rank Intraclass Correlation for Clustered Data
c(person(given = "Shengxin",
family = "Tu",
role = c("aut", "cre"),
email = "shengxin.tu@vanderbilt.edu"),
person(given = "Chun",
family = "Li",
role = "aut"),
person(given = "Donglin",
family = "Zeng",
role = "aut"),
person(given = "Bryan",
family = "Shepherd",
role = "aut"))
Estimates the rank intraclass correlation coefficient (ICC) for clustered continuous, ordinal, binary, and count data <https://github.com/shengxintu/rankICC>.
Shengxin Tu [aut, cre],
Chun Li [aut],
Donglin Zeng [aut],
Bryan Shepherd [aut]

Methods for Working with Uncertainty in Rankings
person(given = "Justin",
family = "Rising",
role = c("aut", "cre"),
email = "justin.rising@us.af.mil")
Provides methods for measuring and describing uncertainty in
rankings.  See Rising (2021) <arXiv:2107.03459> for background.