Missing values are ubiquitous in data and need to be explored and handled in the initial stages of analysis. 'naniar' provides data structures and functions that facilitate the plotting of missing values and examination of imputations. This allows missing data dependencies to be explored with minimal deviation from the common work patterns of 'ggplot2' and tidy data. The work is fully discussed at Tierney & Cook (2023) <doi:10.18637/jss.v105.i07>.
Version: 1.1.0 Depends: R (≥ 3.1.2) Imports: dplyr, ggplot2, purrr, tidyr, tibble (≥ 2.0.0), norm, magrittr, stats, visdat, rlang (≥ 1.1.0), forcats, viridis, glue, UpSetR, cli, vctrs, lifecycle Suggests: knitr, rmarkdown, testthat (≥ 3.0.0), rpart, rpart.plot, covr, gridExtra, wakefield, vdiffr, here, simputation, imputeTS, Hmisc, spelling Published: 2024-03-05 DOI: 10.32614/CRAN.package.naniar Author: Nicholas Tierney [aut, cre], Di Cook [aut], Miles McBain [aut], Colin Fay [aut], Mitchell O'Hara-Wild [ctb], Jim Hester [ctb], Luke Smith [ctb], Andrew Heiss [ctb] Maintainer: Nicholas Tierney <nicholas.tierney at gmail.com> BugReports: https://github.com/njtierney/naniar/issues License: MIT + file LICENSE URL: https://github.com/njtierney/naniar, http://naniar.njtierney.com/ NeedsCompilation: no Language: en-US Citation: naniar citation info Materials: README, NEWS In views: MissingData CRAN checks: naniar results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=naniar to link to this page.
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