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Upsilon  

Another Test of Association for Count Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("Upsilon", "0.1.0")



Attach the package and use:
library("Upsilon")
Maintained by
Joe Song
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-01-06
Latest Update: 2026-01-06
Description:
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 sequencing—an important modern scientific domain.
How to cite:
Joe Song (2026). Upsilon: Another Test of Association for Count Data. R package version 0.1.0, https://cran.r-project.org/web/packages/Upsilon. Accessed 04 Jul. 2026.
Previous versions and publish date:
0.1.0 (2026-01-06 12:40)
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Functions, R codes and Examples using the Upsilon R package
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