This is a set of simple utilities for various data
manipulation and testing tasks.  The goal is to use core R tools well,
without bringing in many dependencies. Main areas of interest are
semi-automated data frame manipulation, such as converting factors in
multiple binary indicator columns. There are testing functions which
provide 'testthat' expectations to permute arguments to function
calls. There are functions and data to test extreme numbers, dates,
and bad input of various kinds which should allow testing failure and
corner cases, which can be used for fuzzing your functions. The test
suite has many examples of usage.
clipr, devtools, knitr, methods, pkgbuild, stats, testthat,
tools, utils, withr

kbest: Finds K best paths in a given graph.
Finds K shortest paths in a given graph.

k Nearest Neighbors with Grid Search Variable Selection
c(person("Eddie", "Pei", email = "ep2667@rit.edu",
role = c("aut", "cre")),
person("Ernest", "Fokoue" , role = c("aut")))
k Nearest Neighbors with variable selection, combine grid search and forward selection to achieve variable selection in order to improve k Nearest Neighbors predictive performance.
Eddie Pei [aut, cre],
Ernest Fokoue [aut]

K-Sample Rank Tests and their Combinations
c(person("Fritz", "Scholz",role=c("aut","cre"),email =
"fscholz@u.washington.edu"), person("Angie", "Zhu", role=c("aut"),
email= "a.zhu.stat@gmail.com"))
Compares k samples using the Anderson-Darling test,
Kruskal-Wallis type tests with different rank score criteria,
and Steel's multiple comparison test.  It computes asymptotic,
simulated or (limited) exact P-values, all valid under
randomization, with or without ties, or conditionally under
random sampling from populations, given the observed tie
pattern.  Except for Steel's test it also combines these tests
across several blocks of samples.  Also analyzed are 2 x t
contingency tables and their blocked combinations using the
Kruskal-Wallis criterion.  Steel's test is inverted to provide
simultaneous confidence bounds for shift parameters.  A
plotting function shows that asymptotic approximations for
small sample sizes provide only very limited utility when
comparing tail probabilities with those obtained via simulation
or exact calculations.
Fritz Scholz [aut, cre],
Angie Zhu [aut]