How to Install and Uninstall r-cran-mlr Package on Kali Linux
Last updated: November 25,2024
1. Install "r-cran-mlr" package
Please follow the steps below to install r-cran-mlr on Kali Linux
$
sudo apt update
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$
sudo apt install
r-cran-mlr
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2. Uninstall "r-cran-mlr" package
Please follow the instructions below to uninstall r-cran-mlr on Kali Linux:
$
sudo apt remove
r-cran-mlr
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$
sudo apt autoclean && sudo apt autoremove
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3. Information about the r-cran-mlr package on Kali Linux
Package: r-cran-mlr
Version: 2.19.1+dfsg-1
Installed-Size: 5925
Maintainer: Debian R Packages Maintainers
Architecture: amd64
Depends: r-base-core (>= 4.2.1-2), r-api-4.0, r-cran-paramhelpers (>= 1.10), r-cran-backports (>= 1.1.0), r-cran-bbmisc (>= 1.11), r-cran-checkmate (>= 1.8.2), r-cran-data.table (>= 1.12.4), r-cran-ggplot2, r-cran-parallelmap (>= 1.3), r-cran-stringi, r-cran-survival, r-cran-xml
Recommends: r-cran-testthat, r-cran-mlbench
Suggests: r-cran-batchtools, r-cran-bit64, r-cran-caret (>= 6.0-57), r-cran-class, r-cran-clue, r-cran-cluster, r-cran-cowplot, r-cran-e1071, r-cran-earth, r-cran-emoa, r-cran-fnn, r-cran-forecast (>= 8.3), r-cran-fpc, r-cran-gbm, r-cran-ggpubr, r-cran-glmnet, r-cran-hmisc, r-cran-irace (>= 2.0), r-cran-kernlab, r-cran-knitr, r-cran-lintr (>= 1.0.0.9001), r-cran-mass, r-cran-mda, r-cran-memoise, r-cran-modeltools, r-cran-nnet, r-cran-numderiv, r-cran-pander, r-cran-party, r-cran-pec, r-cran-pls, r-cran-randomforest, r-cran-ranger (>= 0.8.0), r-cran-rappdirs, r-cran-rex, r-cran-rgenoud, r-cran-rmarkdown, r-cran-rmpi, r-cran-rocr, r-cran-rpart, r-cran-sf, r-cran-svglite, r-cran-tgp, r-cran-th.data, r-cran-tidyr, r-cran-vdiffr
Size: 4932448
SHA256: 59aa58ce5e4e74244a2c2eb28b25757a7aa1707de02e9442d49f5927e05a1916
SHA1: a7a49bb09be736c55093c1127fa4cfd97a1ac690
MD5sum: a26f8babff7c6a80f3ff6dcec733a457
Description: Machine learning in GNU R
Interface to a large number of classification and regression
techniques, including machine-readable parameter descriptions. There is
also an experimental extension for survival analysis, clustering and
general, example-specific cost-sensitive learning. Generic resampling,
including cross-validation, bootstrapping and subsampling. Hyperparameter
tuning with modern optimization techniques, for single- and multi-objective
problems. Filter and wrapper methods for feature selection. Extension of
basic learners with additional operations common in machine learning, also
allowing for easy nested resampling. Most operations can be parallelized.
Description-md5:
Homepage: https://cran.r-project.org/package=mlr
Section: gnu-r
Priority: optional
Filename: pool/main/r/r-cran-mlr/r-cran-mlr_2.19.1+dfsg-1_amd64.deb
Version: 2.19.1+dfsg-1
Installed-Size: 5925
Maintainer: Debian R Packages Maintainers
Architecture: amd64
Depends: r-base-core (>= 4.2.1-2), r-api-4.0, r-cran-paramhelpers (>= 1.10), r-cran-backports (>= 1.1.0), r-cran-bbmisc (>= 1.11), r-cran-checkmate (>= 1.8.2), r-cran-data.table (>= 1.12.4), r-cran-ggplot2, r-cran-parallelmap (>= 1.3), r-cran-stringi, r-cran-survival, r-cran-xml
Recommends: r-cran-testthat, r-cran-mlbench
Suggests: r-cran-batchtools, r-cran-bit64, r-cran-caret (>= 6.0-57), r-cran-class, r-cran-clue, r-cran-cluster, r-cran-cowplot, r-cran-e1071, r-cran-earth, r-cran-emoa, r-cran-fnn, r-cran-forecast (>= 8.3), r-cran-fpc, r-cran-gbm, r-cran-ggpubr, r-cran-glmnet, r-cran-hmisc, r-cran-irace (>= 2.0), r-cran-kernlab, r-cran-knitr, r-cran-lintr (>= 1.0.0.9001), r-cran-mass, r-cran-mda, r-cran-memoise, r-cran-modeltools, r-cran-nnet, r-cran-numderiv, r-cran-pander, r-cran-party, r-cran-pec, r-cran-pls, r-cran-randomforest, r-cran-ranger (>= 0.8.0), r-cran-rappdirs, r-cran-rex, r-cran-rgenoud, r-cran-rmarkdown, r-cran-rmpi, r-cran-rocr, r-cran-rpart, r-cran-sf, r-cran-svglite, r-cran-tgp, r-cran-th.data, r-cran-tidyr, r-cran-vdiffr
Size: 4932448
SHA256: 59aa58ce5e4e74244a2c2eb28b25757a7aa1707de02e9442d49f5927e05a1916
SHA1: a7a49bb09be736c55093c1127fa4cfd97a1ac690
MD5sum: a26f8babff7c6a80f3ff6dcec733a457
Description: Machine learning in GNU R
Interface to a large number of classification and regression
techniques, including machine-readable parameter descriptions. There is
also an experimental extension for survival analysis, clustering and
general, example-specific cost-sensitive learning. Generic resampling,
including cross-validation, bootstrapping and subsampling. Hyperparameter
tuning with modern optimization techniques, for single- and multi-objective
problems. Filter and wrapper methods for feature selection. Extension of
basic learners with additional operations common in machine learning, also
allowing for easy nested resampling. Most operations can be parallelized.
Description-md5:
Homepage: https://cran.r-project.org/package=mlr
Section: gnu-r
Priority: optional
Filename: pool/main/r/r-cran-mlr/r-cran-mlr_2.19.1+dfsg-1_amd64.deb