tibble, stats, ipred, dimRed (>= 0.1.0), lubridate, timeDate,
ddalpha, purrr, rlang (>= 0.1.1), gower, RcppRoll, tidyselect
(>= 0.1.1), magrittr, Matrix
testthat, rpart, kernlab, fastICA, RANN, igraph, knitr,
caret, ggplot2, rmarkdown, covr

c(
person(given = "Max",    family = "Kuhn",    email = "max@rstudio.com",    role = c("aut", "cre")),
person(given = "Hadley", family = "Wickham", email = "hadley@rstudio.com", role = c("aut")),
person("RStudio", role = "cph"))
generics, glue, gower, ipred, lubridate, magrittr, Matrix,
purrr (>= 0.2.3), rlang (>= 0.4.0), stats, tibble, tidyr (>=
0.8.3), tidyselect (>= 0.2.5), timeDate, utils, withr
covr, ddalpha, dimRed (>= 0.2.2), fastICA, ggplot2, igraph,
kernlab, knitr, modeldata, pls, RANN, RcppRoll, rmarkdown,
rpart, rsample, RSpectra, testthat (>= 2.1.0), xml2

generics, glue, gower, ipred, lifecycle, lubridate, magrittr,
Matrix, purrr (>= 0.2.3), rlang (>= 0.4.0), stats, tibble,
tidyr (>= 1.0.0), tidyselect (>= 0.2.5), timeDate, utils, withr
covr, ddalpha, dimRed (>= 0.2.2), fastICA, ggplot2, igraph,
kernlab, knitr, modeldata, RANN, RcppRoll, rmarkdown, rpart,
rsample, RSpectra, testthat (>= 2.1.0), xml2

generics (>= 0.1.0), glue, gower, ipred, lifecycle, lubridate,
magrittr, Matrix, purrr (>= 0.2.3), rlang (>= 0.4.0), stats,
tibble, tidyr (>= 1.0.0), tidyselect (>= 1.1.0), timeDate,
utils, withr

ellipsis, generics (>= 0.1.0), glue, gower, ipred, lifecycle,
lubridate, magrittr, Matrix, purrr (>= 0.2.3), rlang (>=
0.4.0), stats, tibble, tidyr (>= 1.0.0), tidyselect (>= 1.1.0),
timeDate, utils, withr

Preprocessing and Feature Engineering Steps for Modeling
c(person(given = "Max",
family = "Kuhn",
role = c("aut", "cre"),
email = "max@rstudio.com"),
person(given = "Hadley",
family = "Wickham",
role = "aut",
email = "hadley@rstudio.com"),
person(given = "RStudio",
role = "cph"))
A recipe prepares your data for modeling. We provide an extensible
framework for pipeable sequences of feature engineering steps provides
preprocessing tools to be applied to data. Statistical parameters for
the steps can be estimated from an initial data set and then applied
to other data sets. The resulting processed output can then be used as
inputs for statistical or machine learning models.
ellipsis, generics (>= 0.1.0), glue, gower, ipred (>= 0.9-12),
lifecycle, lubridate, magrittr, Matrix, purrr (>= 0.2.3), rlang
(>= 0.4.0), stats, tibble, tidyr (>= 1.0.0), tidyselect (>=
1.1.0), timeDate, utils, vctrs, withr
covr, ddalpha, dimRed (>= 0.2.2), fastICA, ggplot2, igraph,
kernlab, knitr, modeldata, parsnip (>= 0.1.7), RANN, RcppRoll,
rmarkdown, rpart, rsample, RSpectra, testthat (>= 3.0.0),
workflows, xml2