Package: r-cran-broom
Version: 1.0.13+dfsg-2
Architecture: all
Maintainer: Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
Original-Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Installed-Size: 2041
Depends: r-api-4.0, r-cran-backports, r-cran-cli, r-cran-dplyr (>= 1.0.0), r-cran-generics (>= 0.0.2), r-cran-glue, r-cran-lifecycle, r-cran-purrr, r-cran-rlang (>= 1.1.0), r-cran-stringr, r-cran-tibble (>= 3.0.0), r-cran-tidyr (>= 1.0.0)
Recommends: r-cran-aer, r-cran-bbmle, r-cran-boot, r-cran-car (>= 3.1-2), r-cran-cardata, r-cran-caret, r-cran-cluster, r-cran-cmprsk, r-cran-coda, r-cran-covr, r-cran-e1071, r-cran-emmeans, r-cran-epir (>= 2.0.85), r-cran-gam, r-cran-gee, r-cran-geepack, r-cran-ggplot2, r-cran-glmnet, r-cran-gmm, r-cran-hmisc, r-cran-interp, r-cran-irlba, r-cran-knitr, r-cran-ks, r-cran-lavaan (>= 0.6.18), r-cran-leaps, r-cran-lme4, r-cran-lmtest, r-cran-lsmeans, r-cran-maps, r-cran-mass, r-cran-mclust, r-cran-metafor, r-cran-mgcv, r-cran-modeldata, r-cran-muhaz, r-cran-multcomp, r-cran-network, r-cran-nnet, r-cran-ordinal, r-cran-plm, r-cran-psych, r-cran-quantreg, r-cran-rmarkdown, r-cran-robust, r-cran-robustbase, r-cran-rsample, r-cran-sandwich, r-cran-spatialreg, r-cran-spdep, r-cran-spelling, r-cran-survey, r-cran-survival (>= 3.6-4), r-cran-systemfit, r-cran-testthat, r-cran-tseries, r-cran-zoo
Section: gnu-r
Priority: optional
Homepage: https://cran.r-project.org/package=broom
Description: Convert Statistical Objects into Tidy Tibbles
 Summarizes key information about statistical objects in tidy tibbles.
 This makes it easy to report results, create plots and consistently work with
 large numbers of models at once. Broom provides three verbs that each provide
 different types of information about a model. tidy() summarizes information
 about model components such as coefficients of a regression. glance() reports
 information about an entire model, such as goodness of fit measures like
 AIC and BIC. augment() adds information about individual observations to
 a dataset, such as fitted values or influence measures.
