Pipe-Friendly Vector Replacement with Case Statements
Offers a pipe-friendly alternative to the 'dplyr'
functions case_when() and if_else().  These functions accept a vector
as an optional first argument, allowing conditional statements to be
built using the 'magrittr' dot operator.  The functions also coerce
all possible outputs to the same type, meaning you no longer have to
worry about using specific typed variants of NA or explicitly
declaring integer outputs.

cli, covr, crayon, dplyr, nombre, stringi, testthat

c(person(given = "Alexander",
family = "Rossell Hayes",
role = c("aut", "cre", "cph"),
email = "alexander@rossellhayes.com",
comment = c(ORCID = "0000-0001-9412-0457")),
person(given = "Patrice",
family = "Kiener",
role = "ctb",
email = "fattailsr@inmodelia.com",
comment = c("Contributed example for fn_case()", ORCID = "0000-0002-0505-9920")))
Offers a pipe-friendly alternative to the 'dplyr' functions
case_when() and if_else(), as well as a number of user-friendly
simplifications for common use cases.  These functions accept a vector
as an optional first argument, allowing conditional statements to be
built using the 'magrittr' dot operator.  The functions also coerce
all outputs to the same type, meaning you no longer have to worry
about using specific typed variants of NA or explicitly declaring
integer outputs, and evaluate outputs somewhat lazily, so you don't
waste time on long operations that won't be used.
covr, crayon, dplyr, nombre, stringi, testthat (>= 3.0.0)
Alexander Rossell Hayes [aut, cre, cph]
(<https://orcid.org/0000-0001-9412-0457>),
Patrice Kiener [ctb] (Contributed example for fn_case(),
<https://orcid.org/0000-0002-0505-9920>)

covr, crayon, dplyr, nombre, stringi, testthat (>= 3.0.0),
tibble

Incremental Graphlet Counting for Network Optimisation
An efficient and incremental approach for calculating
the differences in orbit counts when performing single edge modifications
in a network. Calculating the differences in orbit counts is much more efficient than
recalculating all orbit counts from scratch for each time point.
dplyr, methods, Rcpp (>= 0.11.4), orca, purrr, testthat,
tibble