tibble, dplyr, messydates, lubridate, purrr, rlang, stringr,
usethis, jsonlite, remotes, httr, progress, ggplot2

tibble, dplyr, messydates, purrr, rlang, stringr, usethis,
jsonlite, remotes, httr, ggplot2 (>= 3.4.0), migraph, cshapes,
ggraph, janitor, tidyr, plyr, zoo

person(given = "Nelson",
family = "Gonzabato",
email = "gonzabato@hotmail.com",
role=c("aut","cre"))
Frequently one needs a convenient way to build and tune,
several models in one go.The goal is to provide a number of convenience functions useful in machine learning
applications. It provides the ability to build,tune and obtain predictions of
several models in one function. The models are built using 'caret' functions with
easier to read syntax.
Kuhn(2014)<arXiv:1405.6974v14>.
Kuhn(2008)<doi10.18637/jss.v028.i05>.
Chambers,J.M.(1992)<doi:10.1371/journal.pone.0053143>.
Wilkinson,G.N. and Rogers, C. E. (1973)<doi:10.2307/2346786>.

Frequently one needs a convenient way to build and tune
several models in one go.The goal is to provide a number of convenience functions useful in machine learning
applications. It provides the ability to build, tune and obtain predictions of
several models in one function. The models are built using 'caret' functions with
easier to read syntax.
Kuhn(2014)<arXiv:1405.6974v14>.
Kuhn(2008)<doi10.18637/jss.v028.i05>.
Chambers,J.M.(1992)<doi:10.1371/journal.pone.0053143>.
Wilkinson,G.N. and Rogers, C. E. (1973)<doi:10.2307/2346786>.

Frequently one needs a convenient way to build and tune
several models in one go.The goal is to provide a number of convenience functions useful in machine learning
applications. It provides the ability to build, tune and obtain predictions of
several models in one function. The models are built using 'caret' functions with
easier to read syntax.
Kuhn(2014) <arXiv:1405.6974v14>.
Kuhn(2008) <doi10.18637/jss.v028.i05>.
Chambers,J.M.(1992) <doi:10.1371/journal.pone.0053143>.
Wilkinson,G.N. and Rogers, C. E. (1973) <doi:10.2307/2346786>.

Frequently one needs a convenient way to build and tune
several models in one go.The goal is to provide a number of convenience functions useful in machine learning
applications. It provides the ability to build, tune and obtain predictions of
several models in one function. The models are built using 'caret' functions with
easier to read syntax.
Kuhn(2014) <arXiv:1405.6974v14>.
Kuhn(2008) <doi10.18637/jss.v028.i05>.
Chambers,J.M.(1992) <doi:10.1007/978-3-642-50096-1_48>.
Wilkinson,G.N. and Rogers, C. E. (1973) <doi:10.2307/2346786>.