Type: Package
Package: tidylo
Title: Weighted Tidy Log Odds Ratio
Version: 0.1.0
Authors@R: 
    c(person(given = "Tyler",
             family = "Schnoebelen",
             role = "aut",
             email = "tjs1976@gmail.com"),
      person(given = "Julia",
             family = "Silge",
             role = c("aut", "cre", "cph"),
             email = "julia.silge@gmail.com",
             comment = c(ORCID = "0000-0002-3671-836X")),
      person(given = "Alex",
             family = "Hayes",
             role = "aut",
             email = "alexpghayes@gmail.com",
             comment = c(ORCID = "0000-0002-4985-5160")))
Description: 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.
License: MIT + file LICENSE
URL: http://github.com/juliasilge/tidylo
BugReports: http://github.com/juliasilge/tidylo/issues
Imports: dplyr, rlang
Suggests: covr, ggplot2, janeaustenr, knitr, rmarkdown, stringr,
        testthat (>= 2.1.0), tidytext
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: TRUE
RoxygenNote: 7.1.0
NeedsCompilation: no
Packaged: 2020-05-16 16:40:27 UTC; juliasilge
Author: Tyler Schnoebelen [aut],
  Julia Silge [aut, cre, cph] (<https://orcid.org/0000-0002-3671-836X>),
  Alex Hayes [aut] (<https://orcid.org/0000-0002-4985-5160>)
Maintainer: Julia Silge <julia.silge@gmail.com>
Repository: CRAN
Date/Publication: 2020-05-25 19:10:03 UTC
