An R framework for constructing and managing human readable regular
expressions.  It aims to provide tools that enable the user to write
regular expressions in a way that is similar to the ways R code is written.
The tools allow the user to (1) write in smaller, modular, named,
sub-expressions, (2) write top to bottom, rather than a single string (3)
comment individual chunks, (4) indent expressions to clearly present regular
expression groups, (5) add vertical line spaces and R comments (i.e., #),
and (6) test the validity of the concatenated expression and the modular
sub-expressions.

An R framework for constructing and managing human
readable regular expressions.  It aims to provide tools that
enable the user to write regular expressions in a way that is
similar to the ways R code is written. The tools allow the user
to (1) write in smaller, modular, named, sub-expressions, (2)
write top to bottom, rather than a single string (3) comment
individual chunks, (4) indent expressions to clearly present
regular expression groups, (5) add vertical line spaces and R
comments (i.e., #), and (6) test the validity of the
concatenated expression and the modular sub-expressions.

Elimination of Noisy Samples in Regression Datasets using Noise
Filters
c(
person(given = "Juan",
family = "Martin",
role = c("aut", "cre"),
email = "juanmartin@usal.es"),
person(given = "José A.",
family = "Sáez",
role = c("aut"),
email = "joseasaezm@ugr.es"),
person(given = "Emilio",
family = "Corchado",
role = c("aut"),
email = "escorchado@usal.es"),
person(given = "Pablo",
family = "Morales",
role = c("ctb"),
comment= "Author of the NoiseFiltersR package"),
person(given = "Julian",
family = "Luengo",
role = c("ctb"),
comment= "Author of the NoiseFiltersR package"),
person(given = "Luis P.F.",
family = "Garcia",
role = c("ctb"),
comment= "Author of the NoiseFiltersR package"),
person(given = "Ana C.",
family = "Lorena",
role = c("ctb"),
comment= "Author of the NoiseFiltersR package"),
person(given = "Andre C.P.L.F.",
family = "de Carvalho",
role = c("ctb"),
comment= "Author of the NoiseFiltersR package"),
person(given = "Francisco",
family = "Herrera",
role = c("ctb"),
comment= "Author of the NoiseFiltersR package")
)
Traditional noise filtering methods aim at removing noisy samples from a classification dataset. This package adapts classic and recent filtering techniques to be used in regression problems. To do this, it uses the approach proposed in Martin (2021) [<doi:10.1109/ACCESS.2021.3123151>]. Thus, the goal of the implemented noise filters is to eliminate samples with noise in regression datasets.
e1071, FNN, gbm, modelr, nnet, randomForest, rpart
Juan Martin [aut, cre],
José A. Sáez [aut],
Emilio Corchado [aut],
Pablo Morales [ctb] (Author of the NoiseFiltersR package),
Julian Luengo [ctb] (Author of the NoiseFiltersR package),
Luis P.F. Garcia [ctb] (Author of the NoiseFiltersR package),
Ana C. Lorena [ctb] (Author of the NoiseFiltersR package),
Andre C.P.L.F. de Carvalho [ctb] (Author of the NoiseFiltersR package),
Francisco Herrera [ctb] (Author of the NoiseFiltersR package)