prep CRAN v0.5.6

R/sentometrics.R

@@ -14,8 +14,8 @@
 #' \item Corpus features generation: \code{\link{sento_corpus}}, \code{\link{add_features}}
 #' \item Sentiment computation and aggregation into sentiment measures: \code{\link{ctr_agg}},
 #' \code{\link{sento_lexicons}}, \code{\link{compute_sentiment}}, \code{\link{aggregate.sentiment}},
-#' \code{\link{sento_measures}}, \code{\link{peakdocs}}, and a series of \code{measures_xyz}, generic
-#' and extractor functions
+#' \code{\link{sento_measures}}, \code{\link{peakdocs}}, \code{\link{peakdates}}, and a series of 
+#' \code{measures_xyz}, generic and extractor functions
 #' \item Sparse modelling: \code{\link{ctr_model}}, \code{\link{sento_model}}
 #' \item Prediction and post-modelling analysis: \code{\link{predict.sentomodel}}, \code{\link{attributions}}
 #' }

cran-comments.md

@@ -3,12 +3,12 @@
 
 **new**: very minor update (one function change and a few documentation fixes)
 
-## submission (version 0.5.5) [15/11/2018]
+<!--- ## submission (version 0.5.5) [15/11/2018]
 
 **new**: minor additions and simplifications
 
 - resolved failing test for old R version 3.4.4
-- diminished the number of Imports
+- diminished the number of Imports --->
 
 <!--- ## submission (version 0.5.1) [20/09/2018]
 

docs/README.md

@@ -17,13 +17,13 @@ Outputs a _`sentocorpus`_ object.
 
 **Step 2**: Choose lexicons and compute document--level textual sentiment
 
-Functions: `sento_lexicons()`, `compute_sentiment()`, `to_sentiment()`, and `sentiment_bind()`.
+Functions: `sento_lexicons()`, `compute_sentiment()`, `to_sentiment()`, `sentiment_bind()` and `peakdocs()`.
 
 Outputs a _`sentolexicons`_ object, and a _`sentiment`_ object or a sentiment scores `data.table`.
 
 **Step 3**: Aggregate the sentiment into multiple textual sentiment time series
 
-Functions: `ctr_agg()`, `sento_measures()`, `plot()`, `aggregate()`, `peakdocs()`, `diff()`, `scale()`, and `summary()`.
+Functions: `ctr_agg()`, `sento_measures()`, `plot()`, `aggregate()`, `peakdates()`, `diff()`, `scale()`, and `summary()`.
 
 A bunch of functions to manipulate the sentiment measures are available (`measures_merge()`, `measures_global()`, `measures_subset()`, `measures_select()`, `measures_delete()`, and `measures_fill()`), as well as a number of convenient extractor functions (`nobs()`, `nmeasures()`, `get_dimensions()`, `get_dates()` and `get_measures()`). Make use of them to facilitate your analysis!