SpiceFP: Sparse Method to Identify Joint Effects of Functional Predictors

A set of functions allowing to implement the 'SpiceFP' approach which is iterative. It involves transformation of functional predictors into several candidate explanatory matrices (based on contingency tables), to which relative edge matrices with contiguity constraints are associated. Generalized Fused Lasso regression are performed in order to identify the best candidate matrix, the best class intervals and related coefficients at each iteration. The approach is stopped when the maximal number of iterations is reached or when retained coefficients are zeros. Supplementary functions allow to get coefficients of any candidate matrix or mean of coefficients of many candidates.

Package details

AuthorGirault Gnanguenon Guesse [aut, cre], Patrice Loisel [aut], Benedicte Fontez [aut], Nadine Hilgert [aut], Thierry Simonneau [ctr], Isabelle Sanchez [ctr]
MaintainerGirault Gnanguenon Guesse <girault.gnanguenon@gmail.com>
Package repositoryView on CRAN
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SpiceFP documentation built on May 11, 2022, 9:06 a.m.