The RcppAlgos package attacks age-old problems in combinatorics and computational mathematics.
The main goal is to encourage fresh and creative approaches to foundational problems. The question that most appropriately summarizes RcppAlgos is: "Can we do better?".
Provide highly optimized functions that facilitates a broader spectrum of researchable cases. E.g
Investigating the structure of large numbers over wide ranges:
primeFactorizeSieve(10^13 - 10^7, 10^13 + 10^7)
primeSieve(2^53 - 10^10, 2^53 - 1, nThreads = 32)
Easily explore combinations/permutations/partitions that would otherwise be inaccessible due to time of execution/memory constraints:
c_iter = comboIter(10000, 100)
bigSamp = gmp::urand.bigz(3, gmp::log2.bigz(comboCount(10000, 100)))
c_iter[[bigSamp]] ## flexible iterator allows random samplingp_iter = partitionsIter(5000, 100, target = 6000)
p_iter[[1e9]] ## start iterating from index = 1e9
p_iter@nextIter()
p_iter@nextNIter(1e3)comboGeneral(150, 5, constraintFun = "sum", Parallel = TRUE)
parallel::mclapply(seq(...), function(x) {
temp = permuteGeneral(15, 10, lower = x, upper = y)
## analyze permutations
## output results
}, mc.cores = detectCores() - 1))partitionsGeneral(0:80, repetition = TRUE)permuteSample(rnorm(100), 10, freqs = rep(1:4, 25), n = 15, seed = 123)
set.seed(123)
comboGeneral(runif(42, 0, 50), 10,
constraintFun = "mean",
comparisonFun = c(">","<"),
limitConstraints = c(39.876, 42.123))Speed!!!.... You will find that the functions in RcppAlgos are some of the fastest of their type available in R.
Joseph Wood