Let’s think a package that has some required dependencies, but its dependencies have some requirements too. The maintainer might consider if he can raise the version requirements.
pd <- package_dependencies("ggeasy")
head(pd)
#> Name Version Type Op Package
#> 1 cli 3.4.0 Imports >= <NA>
#> 2 lifecycle 1.0.3 Imports >= <NA>
#> 3 R 4.4.0 Depends >= <NA>
#> 4 rlang 1.1.0 Imports >= <NA>
#> 5 vctrs 0.6.0 Imports >= <NA>
#> 6 utils <NA> <NA> <NA> <NA>
# Discover the requirements that can be upgraded
update_dependencies(pd)We can also be interested on since when did a package can be installed we can use:
package_date("ggeasy")
#> Published deps_available
#> "2025-06-15 04:40:04 UTC" NAWhich will show when it was published and when the R dependencies required become available.
Other utils
When were packages last updated?
cran_session()
#> Warning in cran_archive(versions[, "Package"]): Omitting packages repo.data.
#> Maybe they were not on CRAN?
#> [1] "2025-07-07"This uses the sessionInfo() output to find the date of
last installation. There is a sister function for an arbitrary data:
versions <- data.frame(Package = c("dplyr", "Rcpp"),
Version = c("1.1.4", "0.8.9"))
versions
#> Package Version
#> 1 dplyr 1.1.4
#> 2 Rcpp 0.8.9
cran_date(versions)
#> [1] "2023-11-17"This date doesn’t mean that all packages are up to date to this given
date (this can be easily checked with old.packages())
Risk of being archived
If you ever wonder which packages are at risk you can use
cran_doom():
cd <- cran_doom(bioc = TRUE)
#> Retrieving CRAN_db, this might take a bit.
#> Caching results to be faster next call in this session.
#> Retrieving bioc_available_release, this might take a bit.
#> Caching results to be faster next call in this session.
cd[c("time_till_last", "last_archived", "npackages")]
#> $time_till_last
#> Time difference of 70 days
#>
#> $last_archived
#> [1] "2025-10-08"
#>
#> $npackages
#> CRAN all
#> 22528 26233
knitr::kable(head(cd$details))| Package | Deadline | type | repo | n_affected |
|---|---|---|---|---|
| ggalt | 2025-07-31 | direct | CRAN | 4 |
| rfars | 2025-07-31 | direct | CRAN | 4 |
| googleLanguageR | 2025-07-31 | direct | CRAN | 3 |
| ClimaRep | 2025-07-31 | direct | CRAN | 2 |
| likert | 2025-07-31 | direct | CRAN | 2 |
| nomisr | 2025-07-31 | direct | CRAN | 2 |
For reproducibility here is the session info:
sessionInfo()
#> R Under development (unstable) (2025-07-27 r88459)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] repo.data_0.0.0.9003
#>
#> loaded via a namespace (and not attached):
#> [1] cli_3.6.5 knitr_1.50 rlang_1.1.6 xfun_0.52
#> [5] textshaping_1.0.1 jsonlite_2.0.0 litedown_0.7 markdown_2.0
#> [9] htmltools_0.5.8.1 ragg_1.4.0 sass_0.4.10 rmarkdown_2.29
#> [13] evaluate_1.0.4 jquerylib_0.1.4 fastmap_1.2.0 yaml_2.3.10
#> [17] lifecycle_1.0.4 compiler_4.6.0 fs_1.6.6 systemfonts_1.2.3
#> [21] digest_0.6.37 R6_2.6.1 commonmark_2.0.0 bslib_0.9.0
#> [25] tools_4.6.0 pkgdown_2.1.3 cachem_1.1.0 desc_1.4.3