Package: RDM 0.1.1

Christopher Strothmann

RDM: Quantify Dependence using Rearranged Dependence Measures

Estimates the rearranged dependence measure ('RDM') of two continuous random variables for different underlying measures. Furthermore, it provides a method to estimate the (SI)-rearrangement copula using empirical checkerboard copulas. It is based on the theoretical results presented in Strothmann et al. (2022) <arxiv:2201.03329> and Strothmann (2021) <doi:10.17877/DE290R-22733>.

Authors:Holger Dette [aut], Karl Friedrich Siburg [aut], Christopher Strothmann [aut, cre], qad contributors [cph]

RDM_0.1.1.tar.gz
RDM_0.1.1.zip(r-4.5)RDM_0.1.1.zip(r-4.4)RDM_0.1.1.zip(r-4.3)
RDM_0.1.1.tgz(r-4.5-x86_64)RDM_0.1.1.tgz(r-4.5-arm64)RDM_0.1.1.tgz(r-4.4-x86_64)RDM_0.1.1.tgz(r-4.4-arm64)RDM_0.1.1.tgz(r-4.3-x86_64)RDM_0.1.1.tgz(r-4.3-arm64)
RDM_0.1.1.tar.gz(r-4.5-noble)RDM_0.1.1.tar.gz(r-4.4-noble)
RDM_0.1.1.tgz(r-4.4-emscripten)RDM_0.1.1.tgz(r-4.3-emscripten)
RDM.pdf |RDM.html✨
RDM/json (API)

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

Bug tracker:https://github.com/christopherstrothmann/rdm/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

2.70 score 3 scripts 173 downloads 6 exports 5 dependencies

Last updated 2 years agofrom:cc1ccacd43. Checks:4 OK, 8 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 25 2025
R-4.5-win-x86_64NOTEMar 25 2025
R-4.5-mac-x86_64NOTEMar 25 2025
R-4.5-mac-aarch64NOTEMar 25 2025
R-4.5-linux-x86_64NOTEMar 25 2025
R-4.4-win-x86_64NOTEMar 25 2025
R-4.4-mac-x86_64NOTEMar 25 2025
R-4.4-mac-aarch64NOTEMar 25 2025
R-4.4-linux-x86_64NOTEMar 25 2025
R-4.3-win-x86_64OKMar 25 2025
R-4.3-mac-x86_64OKMar 25 2025
R-4.3-mac-aarch64OKMar 25 2025

Exports:checkerboardDensitycheckerboardDensityIndexcomputeBandwidthcomputeCBMeasurerdmsortDSMatrix

Dependencies:RcppRcppArmadilloRcppParallelRfastzigg