**Steiner Tree Approach for Graph Analysis** (**SteinerNet**)

A set of graph functions to find Steiner trees on graphs. It provides tools for analysing Steiner tree application on networks. It has applications in biological pathway network analysis (Sadeghi 2013) <doi:10.1186/1471-2105-14-144>.

**Organizing Data in a Hypercube** (**hypercube**)

Provides methods for organizing data in a hypercube (i.e. a multi-dimensional cube). Cubes are generated from molten data frames. Each cube can be manipulated with five operations: rotation (changeDimensionOrder()), dicing and slicing (add.selection(), remove.selection()), drilling down (add.aggregation()), and rolling up (remove.aggregation()).

**Binning Variables to Use in Logistic Regression** (**logiBin**)

Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) <doi:10.1198/106186006X133933>.

**Projection Pursuit Classification Forest** (**PPforest**)

Implements projection pursuit forest algorithm for supervised classification.

**Simulations of Matrix Variate Distributions** (**matrixsampling**)

Provides samplers for various matrix variate distributions: Wishart, inverse-Wishart, normal, t, inverted-t, Beta type I and Beta type II. Allows to simulate the noncentral Wishart distribution without the integer restriction on the degrees of freedom.

**Effect Modification in Observational Studies Using the Submax Method** (**submax**)

Effect modification occurs if a treatment effect is larger or more stable in certain subgroups defined by observed covariates. The submax or subgroup-maximum method of Lee et al. (2017) <arXiv:1702.00525> does an overall test and separate tests in subgroups, correcting for multiple testing using the joint distribution.

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