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RcppAlgos  

High Performance Tools for Combinatorics and Computational Mathematics
View on CRAN: Click here


Download and install RcppAlgos package within the R console
Install from CRAN:
install.packages("RcppAlgos")

Install from Github:
library("remotes")
install_github("cran/RcppAlgos")

Install by package version:
library("remotes")
install_version("RcppAlgos", "2.9.3")



Attach the package and use:
library("RcppAlgos")
Maintained by
Joseph Wood
[Scholar Profile | Author Map]
First Published: 2017-10-26
Latest Update: 2023-12-10
Description:
Provides optimized functions and flexible combinatorial iterators implemented in C++ for solving problems in combinatorics and computational mathematics. Utilizes the RMatrix class from 'RcppParallel' for thread safety. There are combination/permutation functions with constraint parameters that allow for generation of all results of a vector meeting specific criteria (e.g. generating integer partitions/compositions or finding all combinations such that the sum is between two bounds). Capable of generating specific combinations/permutations (e.g. retrieve only the nth lexicographical result) which sets up nicely for parallelization as well as random sampling. Gmp support permits exploration where the total number of results is large (e.g. comboSample(10000, 500, n = 4)). Additionally, there are several high performance number theoretic functions that are useful for problems common in computational mathematics. Some of these functions make use of the fast integer division library 'libdivide'. The primeSieve function is based on the segmented sieve of Eratosthenes implementation by Kim Walisch. It is also efficient for large numbers by using the cache friendly improvements originally developed by Tom
How to cite:
Joseph Wood (2017). RcppAlgos: High Performance Tools for Combinatorics and Computational Mathematics. R package version 2.9.3, https://cran.r-project.org/web/packages/RcppAlgos. Accessed 26 Mar. 2025.
Previous versions and publish date:
0.1.0 (2017-10-26 19:19), 0.1.1 (2017-11-03 05:28), 0.1.2 (2017-11-03 05:32), 0.2.0 (2017-11-28 23:04), 0.2.1 (2017-11-30 00:42), 0.2.2 (2017-12-15 06:17), 0.2.4 (2017-12-18 13:00), 0.2.5 (2018-01-04 15:47), 1.0.0 (2018-03-07 22:16), 1.0.1 (2018-03-10 18:01), 2.0.1 (2018-06-10 18:43), 2.0.2 (2018-06-14 16:00), 2.0.3 (2018-06-30 19:04), 2.1.0 (2018-09-11 10:10), 2.2.0 (2018-09-15 00:22), 2.3.0 (2019-03-17 11:13), 2.3.1 (2019-03-21 11:43), 2.3.2 (2019-04-14 12:32), 2.3.3 (2019-06-30 07:11), 2.3.4 (2019-07-03 10:00), 2.3.5 (2020-01-26 07:40), 2.3.6 (2020-01-29 16:10), 2.4.1 (2020-03-24 08:10), 2.4.3 (2021-05-30 21:40), 2.5.0 (2022-03-16 23:20), 2.5.3 (2022-03-31 09:50), 2.6.0 (2022-08-15 12:50), 2.7.1 (2023-02-06 09:12), 2.7.2 (2023-02-11 14:40), 2.8.0 (2023-07-11 01:40), 2.8.1 (2023-08-14 16:30), 2.8.2 (2023-10-03 01:50), 2.8.3 (2023-12-10 02:10), 2.8.5 (2024-10-11 07:10), 2.9.1 (2025-01-27 10:10)
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