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.
|Author||Gilles Kratzer [aut, cre] (<https://orcid.org/0000-0002-5929-8935>), Reinhard Furrer [ctb] (<https://orcid.org/0000-0002-6319-2332>)|
|Maintainer||Gilles Kratzer <email@example.com>|
|Package repository||View on CRAN|
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