**Heuristics Tools Based on Mutual Information for Variable Ranking** (**varrank**)

A computational toolbox of heuristics approaches for performing variable ranking and feature selection based on mutual information well adapted for multivariate system epidemiology datasets. The core function is a general implementation of the minimum redundancy maximum relevance model. R. Battiti (1994) <doi:10.1109/72.298224>. Continuous variables are discretized using a large choice of rule. Variables ranking can be learned with a sequential forward/backward search algorithm. The two main problems that can be addressed by this package is the selection of the most representative variable within a group of variables of interest (i.e. dimension reduction) and variable ranking with respect to a set of features of interest.

**The Expectation-Maximization Approach to Bayesian Variable Selection** (**EMVS**)

An efficient expectation-maximization algorithm for fitting Bayesian spike-and-slab regularization paths for linear regression. Rockova and George (2014) <doi:10.1080/01621459.2013.869223>.

**Determine a Script’s Filename from Within the Script Itself** (**scriptName**)

A small set of functions wrapping up the call stack and command line inspection needed to determine a running script’s filename from within the script itself.

**Finds the Best Subset of Points to Sample** (**NITPicker**)

Given a few examples of experiments over a time (or spatial) course, ‘NITPicker’ selects a subset of points to sample in follow-up experiments, which would (i) best distinguish between the experimental conditions and the control condition (ii) best distinguish between two models of how the experimental condition might differ from the control (iii) a combination of the two. Ezer and Keir (2018) <doi:10.1101/301796>.

**Visualizing Social Science Data with ‘ggplot2’** (**ggpol**)

A ‘ggplot2’ extension for implementing parliament charts and several other useful visualizations.

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