Feature subset selection algorithms modularized in search algorithms and measure utilities.

Performs fast detection of interactions in large-scale data using the
    method of random intersection trees introduced in
    Shah, R. D. and Meinshausen, N. (2014) <http://www.jmlr.org/papers/v15/shah14a.html>. 
    The algorithm finds potentially high-order interactions in high-dimensional binary
    two-class classification data, without requiring lower order interactions
    to be informative.  The search is particularly fast when the matrices of
    predictors are sparse.  It can also be used to perform market basket analysis
    when supplied with a single binary data matrix.  Here it will find collections
    of columns which for many rows contain all 1's.

Functions for importing, creating, editing and
    exporting 'FSK' files <https://foodrisklabs.bfr.bund.de/fskx-food-safety-knowledge-exchange-format/>
    using the 'R' programming environment. Furthermore, it enables users
    to run simulations contained in the 'FSK' files and visualize the results.

Wrapper functions that interface with 'FSL' 
    <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/>, a powerful and commonly-used 'neuroimaging'
    software, using system commands. The goal is to be able to interface with 'FSL'
    completely in R, where you pass R objects of class 'nifti', implemented by
    package 'oro.nifti', and the function executes an 'FSL' command and returns an R
    object of class 'nifti' if desired.

Randomized and balanced allocation of units to treatment groups using the Finite Selection Model (FSM). The FSM was originally proposed and developed at the RAND corporation by Carl Morris to enhance the experimental design for the now famous Health Insurance Experiment. See Morris (1979) <doi:10.1016/0304-4076(79)90053-8> for details on the original version of the FSM.

Estimation of Rosenthal's fail safe number including confidence intervals. The relevant papers are the following. Konstantinos C. Fragkos, Michail Tsagris and Christos C. Frangos (2014). "Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal's Fail-Safe Number". International Scholarly Research Notices, Volume 2014. <doi:10.1155/2014/825383>. Konstantinos C. Fragkos, Michail Tsagris and Christos C. Frangos (2017). "Exploring the distribution for the estimator of Rosenthal's fail-safe number of unpublished studies in meta-analysis". Communications in Statistics-Theory and Methods, 46(11):5672--5684. <doi:10.1080/03610926.2015.1109664>.