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Package: mlr3fda
Title: Extending 'mlr3' to Functional Data Analysis
Version: 0.7.0.9000
Authors@R: c(
person("Maximilian", "Mücke", , "muecke.maximilian@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0009-0000-9432-9795")),
person("Sebastian", "Fischer", , "sebf.fischer@gmail.com", role = "aut",
comment = c(ORCID = "0000-0002-9609-3197")),
person("Fabian", "Scheipl", , "fabian.scheipl@googlemail.com", role = "ctb",
comment = c(ORCID = "0000-0001-8172-3603")),
person("Bernd", "Bischl", , "bernd_bischl@gmx.net", role = "ctb",
comment = c(ORCID = "0000-0001-6002-6980"))
)
Description: Extends the 'mlr3' ecosystem to functional analysis by adding
support for irregular and regular functional data as defined in the
'tf' package. The package provides 'PipeOps' for preprocessing
functional columns and for extracting scalar features, thereby
allowing standard machine learning algorithms to be applied
afterwards. Available operations include simple functional features
such as the mean or maximum, smoothing, interpolation, flattening, and
functional 'PCA'.
License: LGPL-3
URL: https://mlr3fda.mlr-org.com, https://github.com/mlr-org/mlr3fda
BugReports: https://github.com/mlr-org/mlr3fda/issues
Depends:
mlr3 (>= 1.3.0),
mlr3pipelines (>= 0.11.0),
R (>= 4.1.0)
Imports:
checkmate,
data.table (>= 1.18.0),
lgr,
mlr3misc (>= 0.19.0),
paradox,
R6,
tf (>= 0.4.0)
Suggests:
FDboost,
lme4,
mboost,
Rcatch22,
rpart,
testthat (>= 3.3.0),
tsfeatures,
wavelets,
withr
Config/roxygen2/markdown: TRUE
Config/roxygen2/r6: TRUE
Config/roxygen2/version: 8.0.0
Config/testthat/edition: 3
Encoding: UTF-8
LazyData: true
Collate:
'zzz.R'
'PipeOpFDABsignal.R'
'PipeOpFDACatch22.R'
'PipeOpFDACor.R'
'PipeOpFDADepth.R'
'PipeOpFDADerive.R'
'PipeOpFDAExtract.R'
'PipeOpFDAFlatten.R'
'PipeOpFDAFourier.R'
'PipeOpFDAIntegrate.R'
'PipeOpFDAInterpol.R'
'PipeOpFDARandomEffect.R'
'PipeOpFDARegister.R'
'PipeOpFDAScaleRange.R'
'PipeOpFDASmooth.R'
'PipeOpFDATsfeatures.R'
'PipeOpFDAWavelets.R'
'PipeOpFDAZoom.R'
'PipeOpFPCA.R'
'TaskClassif_phoneme.R'
'TaskRegr_dti.R'
'TaskRegr_fuel.R'
'bibentries.R'
'datasets.R'
'hash_input.R'