How to Install and Uninstall r-cran-recipes Package on Ubuntu 21.10 (Impish Indri)
Last updated: November 22,2024
1. Install "r-cran-recipes" package
Please follow the step by step instructions below to install r-cran-recipes on Ubuntu 21.10 (Impish Indri)
$
sudo apt update
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$
sudo apt install
r-cran-recipes
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2. Uninstall "r-cran-recipes" package
Please follow the steps below to uninstall r-cran-recipes on Ubuntu 21.10 (Impish Indri):
$
sudo apt remove
r-cran-recipes
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$
sudo apt autoclean && sudo apt autoremove
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3. Information about the r-cran-recipes package on Ubuntu 21.10 (Impish Indri)
Package: r-cran-recipes
Architecture: all
Version: 0.1.15+dfsg-1
Priority: optional
Section: universe/gnu-r
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 1627
Depends: r-base-core (>= 4.0.3-1), r-api-4.0, r-cran-dplyr, r-cran-generics (>= 0.1.0), r-cran-glue, r-cran-gower, r-cran-ipred, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-matrix, r-cran-purrr (>= 0.2.3), r-cran-rlang (>= 0.4.0), r-cran-tibble, r-cran-tidyr (>= 1.0.0), r-cran-tidyselect (>= 1.1.0), r-cran-timedate, r-cran-withr
Recommends: r-cran-testthat (>= 2.1.0), r-cran-xml2, r-cran-ddalpha, r-cran-dimred, r-cran-fastica, r-cran-kernlab, r-cran-pls, r-cran-rcpproll, r-cran-rsample, r-cran-modeldata, r-cran-rspectra, r-cran-igraph, r-cran-rann
Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-rpart
Filename: pool/universe/r/r-cran-recipes/r-cran-recipes_0.1.15+dfsg-1_all.deb
Size: 1260548
MD5sum: 4671b68e7d8eb3f2940bfd33b3dd3369
SHA1: 7e784d53cd72df46f69ce686efa3ec6ad157a546
SHA256: 72675b337adb7b7f359b09c5d9aa34f45d27053e74f5102a2e61ac32c4cd93af
SHA512: ba8a07780fd5a519d4c7f8c1425d9dd791cb31913f734d02c5c478402901a2ef8d0d80381ce399c994e5f80298e8b0c03f6d818dffdfe49b583b65ff9fe0982f
Homepage: https://cran.r-project.org/package=recipes
Description-en: GNU R preprocessing tools to create design matrices
This GNU R package provides an extensible framework to create and
preprocess design matrices. Recipes consist of one or more data
manipulation and analysis "steps". Statistical parameters for the steps
can be estimated from an initial data set and then applied to other data
sets. The resulting design matrices can then be used as inputs into
statistical or machine learning models.
Description-md5: cd7fdab093dd07718f27cfdb087cfce4
Architecture: all
Version: 0.1.15+dfsg-1
Priority: optional
Section: universe/gnu-r
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 1627
Depends: r-base-core (>= 4.0.3-1), r-api-4.0, r-cran-dplyr, r-cran-generics (>= 0.1.0), r-cran-glue, r-cran-gower, r-cran-ipred, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-matrix, r-cran-purrr (>= 0.2.3), r-cran-rlang (>= 0.4.0), r-cran-tibble, r-cran-tidyr (>= 1.0.0), r-cran-tidyselect (>= 1.1.0), r-cran-timedate, r-cran-withr
Recommends: r-cran-testthat (>= 2.1.0), r-cran-xml2, r-cran-ddalpha, r-cran-dimred, r-cran-fastica, r-cran-kernlab, r-cran-pls, r-cran-rcpproll, r-cran-rsample, r-cran-modeldata, r-cran-rspectra, r-cran-igraph, r-cran-rann
Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-rpart
Filename: pool/universe/r/r-cran-recipes/r-cran-recipes_0.1.15+dfsg-1_all.deb
Size: 1260548
MD5sum: 4671b68e7d8eb3f2940bfd33b3dd3369
SHA1: 7e784d53cd72df46f69ce686efa3ec6ad157a546
SHA256: 72675b337adb7b7f359b09c5d9aa34f45d27053e74f5102a2e61ac32c4cd93af
SHA512: ba8a07780fd5a519d4c7f8c1425d9dd791cb31913f734d02c5c478402901a2ef8d0d80381ce399c994e5f80298e8b0c03f6d818dffdfe49b583b65ff9fe0982f
Homepage: https://cran.r-project.org/package=recipes
Description-en: GNU R preprocessing tools to create design matrices
This GNU R package provides an extensible framework to create and
preprocess design matrices. Recipes consist of one or more data
manipulation and analysis "steps". Statistical parameters for the steps
can be estimated from an initial data set and then applied to other data
sets. The resulting design matrices can then be used as inputs into
statistical or machine learning models.
Description-md5: cd7fdab093dd07718f27cfdb087cfce4