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]

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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'))

Peer review:

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

On CRAN:

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

1 exports 94 stars 7.30 score 16 dependencies 140 scripts 377 downloads

Last updated 3 years agofrom:5b8d9b0dc9. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 06 2024
R-4.5-winOKSep 06 2024
R-4.5-linuxOKSep 06 2024
R-4.4-winOKSep 06 2024
R-4.4-macOKSep 06 2024
R-4.3-winOKSep 06 2024
R-4.3-macOKSep 06 2024

Exports:bind_log_odds

Dependencies:clidplyrfansigenericsgluelifecyclemagrittrpillarpkgconfigR6rlangtibbletidyselectutf8vctrswithr

Tidy Log Odds

Rendered fromtidylo.Rmdusingknitr::rmarkdownon Sep 06 2024.

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