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Review 2 from CRAN
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juliasilge committed May 16, 2020
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4 changes: 2 additions & 2 deletions DESCRIPTION
Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@ Authors@R:
email = "[email protected]"),
person(given = "Julia",
family = "Silge",
role = c("aut", "cre"),
role = c("aut", "cre", "cph"),
email = "[email protected]",
comment = c(ORCID = "0000-0002-3671-836X")),
person(given = "Alex",
Expand All @@ -22,7 +22,7 @@ Description: How can we measure how the usage or frequency of some feature, such
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
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,
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69 changes: 69 additions & 0 deletions cran-comments.md
Original file line number Diff line number Diff line change
Expand Up @@ -38,3 +38,72 @@ auto-linking.
Add to the Description:

"...here we use the method outlined in Monroe, Colaresi, and Quinn (2008) <doi:10.1093/pan/mpn018>"

### Review 2 - 2020-05-16

> Please always write package names, software names and API (application
programming interface) names in single quotes in title and description.
e.g: --> 'tidylow'

Changed the Description from:

"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."

to:

"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."

> Some authors seem also to be copyright holders [cph].
Please add this information to the Authors@R field.

Changed the Authors@R from:

c(person(given = "Tyler",
family = "Schnoebelen",
role = "aut",
email = "[email protected]"),
person(given = "Julia",
family = "Silge",
role = c("aut", "cre"),
email = "[email protected]",
comment = c(ORCID = "0000-0002-3671-836X")),
person(given = "Alex",
family = "Hayes",
role = "aut",
email = "[email protected]",
comment = c(ORCID = "0000-0002-4985-5160")))

to:

c(person(given = "Tyler",
family = "Schnoebelen",
role = "aut",
email = "[email protected]"),
person(given = "Julia",
family = "Silge",
role = c("aut", "cre", "cph"),
email = "[email protected]",
comment = c(ORCID = "0000-0002-3671-836X")),
person(given = "Alex",
family = "Hayes",
role = "aut",
email = "[email protected]",
comment = c(ORCID = "0000-0002-4985-5160")))

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