pdcor: Fast and Light-Weight Partial Distance Correlation
Fast and memory-less computation of the partial distance correlation for vectors and matrices. Permutation-based and asymptotic hypothesis testing for zero partial distance correlation are also performed. References include: Szekely G. J. and Rizzo M. L. (2014). "Partial distance correlation with methods for dissimilarities". The Annals Statistics, 42(6): 2382–2412. <doi:10.1214/14-AOS1255>. Shen C., Panda S. and Vogelstein J. T. (2022). "The Chi-Square Test of Distance Correlation". Journal of Computational and Graphical Statistics, 31(1): 254–262. <doi:10.1080/10618600.2021.1938585>. Szekely G. J. and Rizzo M. L. (2023). "The Energy of Data and Distance Correlation". Chapman and Hall/CRC. <ISBN:9781482242744>. Kontemeniotis N., Vargiakakis R. and Tsagris M. (2025). On independence testing using the (partial) distance correlation. <doi:10.48550/arXiv.2506.15659>.
| Version: |
1.2 |
| Depends: |
R (≥ 4.0) |
| Imports: |
dcov, Rfast, Rfast2, stats |
| Published: |
2025-07-02 |
| DOI: |
10.32614/CRAN.package.pdcor |
| Author: |
Michail Tsagris [aut, cre],
Nikolaos Kontemeniotis [aut] |
| Maintainer: |
Michail Tsagris <mtsagris at uoc.gr> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| CRAN checks: |
pdcor results |
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