I would not include the ribbons in the last plot. But here is an alternative way of looking at things from an infrastructure perspective
which is essentially Googleâ€™s original algorithm applied to CRAN packages. If you do
install.packages("https://github.com/andrie/pagerank/archive/master.tar.gz",
repos = NULL)
library(pagerank)
pr < compute_pagerank("https://cran.rstudio.com", decreasing = TRUE)
head(pr, 80)
The rstan package is currently 80th out of 16119 packages
Rcpp ggplot2 MASS dplyr Matrix magrittr stringr mvtnorm
0.0292606801 0.0141961468 0.0127345517 0.0107156933 0.0075686855 0.0069382619 0.0061439859 0.0055342496
data.table RcppArmadillo jsonlite tibble rlang survival plyr httr
0.0054958643 0.0054150667 0.0053252688 0.0051572938 0.0051009970 0.0050220072 0.0046550127 0.0046362659
tidyr purrr shiny foreach igraph sp lattice reshape2
0.0042626508 0.0040827308 0.0039510623 0.0037369293 0.0037330274 0.0034590004 0.0031187486 0.0030352190
doParallel raster lubridate scales zoo RColorBrewer coda R6
0.0026634119 0.0024912280 0.0024265575 0.0023214226 0.0023009021 0.0022627788 0.0021832618 0.0021770948
xml2 gridExtra knitr glmnet nlme boot readr numDeriv
0.0020232967 0.0020228031 0.0019229942 0.0018524435 0.0018318426 0.0018191508 0.0018061785 0.0017807931
RcppEigen XML mgcv lme4 ape digest assertthat RCurl
0.0017152923 0.0017104067 0.0016335523 0.0016284246 0.0016136271 0.0015780888 0.0015517581 0.0015164083
pracma glue rgl Rdpack Hmisc gtools htmltools BH
0.0015020734 0.0014736499 0.0014718172 0.0014712523 0.0014693716 0.0014411179 0.0014252742 0.0013906720
curl cluster rgdal stringi car rJava rmarkdown Formula
0.0013880809 0.0013225083 0.0012620477 0.0012600271 0.0012570148 0.0012358317 0.0012285247 0.0012176682
htmlwidgets abind sf crayon fields e1071 plotly DBI
0.0012108465 0.0012059995 0.0011814621 0.0011472009 0.0011197394 0.0010698513 0.0010401082 0.0009954857
checkmate nnet quadprog matrixStats randomForest vegan rpart rstan
0.0009791682 0.0009676665 0.0009645115 0.0009575255 0.0009241805 0.0009160678 0.0009123694 0.0009026298
which a pagerank that is 14.55 times the average package
pr["rstan"] / mean(pr) # 14.54949
Most of the packages that are more important than rstan on this metric are utilities, rather than statistics. MASS, survival, mgcv, cluster, nnet, and rpart statistical packages that come with the default installation of R, which means they are broadly useful but have a leg up on all the nonrecommended packages. The other packages of note I think are

coda (31st): Has been around a long time but the posterior package should be better

glmnet (36th): A supervised learning package that emphasizes elastic net penalization

lme4 (44th): A package for estimating Frequentist hierarchical models

randomForest (77th): The canonical implementation (in R) of the most popular supervised learning approach these days
I think it is amazing that (R)
Stan is essentially as fundamental to Bayesian modeling as randomForest is to supervised learning, but Bayesian modeling has been overtaken (by a lot) by supervised learning approaches during the decade since Stan has been developed.