`RcppAlgos-package.Rd`

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 sampling`

`p_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`

.