Package: SpiceFP
Type: Package
Title: Sparse Method to Identify Joint Effects of Functional Predictors
Version: 0.1.0
Author: Girault Gnanguenon Guesse [aut, cre],
  Patrice Loisel [aut],
  Benedicte Fontez [aut],
  Nadine Hilgert [aut],
  Thierry Simonneau [ctr],
  Isabelle Sanchez [ctr]
Authors@R: c(
  person("Girault", "Gnanguenon Guesse", email = "girault.gnanguenon@gmail.com", role = c("aut", "cre")),
  person("Patrice", "Loisel", email = "patrice.loisel@inrae.fr", role = c("aut")),
  person("Benedicte", "Fontez", email = "benedicte.fontez@supagro.fr", role = c("aut")),
  person("Nadine", "Hilgert", email = "nadine.hilgert@inrae.fr", role = c("aut")),
  person("Thierry", "Simonneau", email = "thierry.simonneau@inrae.fr", role = c("ctr")),
  person("Isabelle", "Sanchez", email = "isabelle.sanchez@inrae.fr", role = c("ctr")))
Maintainer: Girault Gnanguenon Guesse <girault.gnanguenon@gmail.com>
Description: 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. <https://hal.archives-ouvertes.fr/hal-03298977>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.6.0)
Imports: doParallel, foreach, stringr, tidyr, Matrix, genlasso, purrr,
        gplots
Suggests: rmarkdown, knitr, fields
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-09-14 11:52:11 UTC; sanchezi
Repository: CRAN
Date/Publication: 2021-09-15 08:40:18 UTC
