Package: tidylo 0.2.0.9000

tidylo: Weighted Tidy Log Odds Ratio

How can we measure how the usage or frequency of some feature, such as words, differs across some group or set, such as documents? One option is to use the log odds ratio, but the log odds ratio alone does not account for sampling variability; we haven't counted every feature the same number of times so how do we know which differences are meaningful? Enter the weighted log odds, which 'tidylo' provides an implementation for, using tidy data principles. In particular, here we use the method outlined in Monroe, Colaresi, and Quinn (2008) <doi:10.1093/pan/mpn018> to weight the log odds ratio by a prior. By default, the prior is estimated from the data itself, an empirical Bayes approach, but an uninformative prior is also available.

Authors:Tyler Schnoebelen [aut], Julia Silge [aut, cre, cph], Alex Hayes [aut]

tidylo_0.2.0.9000.tar.gz
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tidylo.pdf |tidylo.html
tidylo/json (API)
NEWS

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

Bug tracker:https://github.com/juliasilge/tidylo/issues

Pkgdown site:https://juliasilge.github.io

On CRAN:

Conda:

empirical-bayeslog-odds-ratiotidy-datatidyverseweighted-log-odds

7.35 score 96 stars 157 scripts 276 downloads 1 exports 16 dependencies

Last updated 3 years agofrom:5b8d9b0dc9. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 05 2025
R-4.5-winOKMar 05 2025
R-4.5-macOKMar 05 2025
R-4.5-linuxOKMar 05 2025
R-4.4-winOKMar 05 2025
R-4.4-macOKMar 05 2025
R-4.4-linuxOKMar 05 2025
R-4.3-winOKMar 05 2025
R-4.3-macOKMar 05 2025

Exports:bind_log_odds

Dependencies:clidplyrfansigenericsgluelifecyclemagrittrpillarpkgconfigR6rlangtibbletidyselectutf8vctrswithr

Tidy Log Odds

Rendered fromtidylo.Rmdusingknitr::rmarkdownon Mar 05 2025.

Last update: 2022-03-22
Started: 2022-03-22