Upsilon: Another Test of Association for Count Data
The Upsilon test assesses association among
categorical variables against the null hypothesis of
independence (Luo 2021 MS thesis; ProQuest Publication
No. 28649813). While promoting dominant function patterns,
it demotes non-dominant function patterns. It is robust
to low expected count—continuity correction
like Yates's seems unnecessary. Using a common null
population following a uniform distribution, contingency
tables are comparable by statistical significance—not
the case for most association tests defining a varying
null population by tensor product of observed marginals.
Although Pearson's chi-squared test, Fisher's exact test,
and Woolf's G-test (related to mutual information) are
useful in some contexts, the Upsilon test appeals to
ranking association patterns not necessarily following
same marginal distributions, such as in count data
from DNA and RNA sequencing—a rapidly expanding frontier
in modern science.
| Version: |
0.1.1 |
| Imports: |
Rcpp (≥ 1.0.8), Rdpack, ggplot2 (≥ 3.4.0) |
| LinkingTo: |
Rcpp |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0), DescTools, USP, metan, FunChisq, patchwork |
| Published: |
2026-03-07 |
| DOI: |
10.32614/CRAN.package.Upsilon |
| Author: |
Xuye Luo [aut],
Joe Song  [aut,
cre] |
| Maintainer: |
Joe Song <joemsong at nmsu.edu> |
| License: |
LGPL (≥ 3) |
| NeedsCompilation: |
yes |
| Materials: |
NEWS |
| CRAN checks: |
Upsilon results |
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