Package: kendallknight 0.4.0

Mauricio Vargas Sepulveda

kendallknight: Efficient Implementation of Kendall's Correlation Coefficient Computation

The computational complexity of the implemented algorithm for Kendall's correlation is O(n log(n)), which is faster than the base R implementation with a computational complexity of O(n^2). For small vectors (i.e., less than 100 observations), the time difference is negligible. However, for larger vectors, the speed difference can be substantial and the numerical difference is minimal. The references are Knight (1966) <doi:10.2307/2282833>, Abrevaya (1999) <doi:10.1016/S0165-1765(98)00255-9>, Christensen (2005) <doi:10.1007/BF02736122> and Emara (2024) <https://learningcpp.org/>. This implementation is described in Vargas Sepulveda (2024) <doi:10.48550/arXiv.2408.09618>.

Authors:Mauricio Vargas Sepulveda [aut, cre], Loader Catherine [ctb], Ross Ihaka [ctb]

kendallknight_0.4.0.tar.gz
kendallknight_0.4.0.tar.gz(r-4.5-noble)kendallknight_0.4.0.tar.gz(r-4.4-noble)
kendallknight_0.4.0.tgz(r-4.4-emscripten)kendallknight_0.4.0.tgz(r-4.3-emscripten)
kendallknight.pdf |kendallknight.html
kendallknight/json (API)
NEWS

# Install 'kendallknight' in R:
install.packages('kendallknight', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/pachadotdev/capybara/issues

Pkgdown:https://pacha.dev

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • cigarettes - Life expectancy and cigarettes per day

cppopenmp

2.70 score 4 scripts 61 downloads 2 exports 1 dependencies

Last updated 15 days agofrom:158a20eee7. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 25 2024
R-4.5-linux-x86_64OKNov 25 2024

Exports:kendall_corkendall_cor_test

Dependencies:cpp11

Basic 'kendallknight' usage

Rendered fromusage.Rmdusingknitr::rmarkdownon Nov 25 2024.

Last update: 2024-11-21
Started: 2024-11-21