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.
| Version: |
0.1.1 |
| Depends: |
R (≥ 3.6.0) |
| Imports: |
doParallel, foreach, stringr, tidyr, Matrix, genlasso, purrr |
| Suggests: |
rmarkdown, knitr, fields |
| Published: |
2022-05-11 |
| Author: |
Girault Gnanguenon Guesse [aut, cre],
Patrice Loisel [aut],
Benedicte Fontez [aut],
Nadine Hilgert [aut],
Thierry Simonneau [ctr],
Isabelle Sanchez [ctr] |
| Maintainer: |
Girault Gnanguenon Guesse <girault.gnanguenon at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
SpiceFP results |
Documentation:
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