Review 2 from CRAN

DESCRIPTION

@@ -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",
@@ -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, 

cran-comments.md

@@ -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")))