Package: sentometrics
Type: Package
Title: An Integrated Framework for Textual Sentiment Time Series
        Aggregation and Prediction
Version: 0.3.5
Authors@R: c(person("David", "Ardia", email = "david.ardia@unine.ch", role = c("aut")),
  person("Keven", "Bluteau", email = "keven.bluteau@unine.ch", role = c("aut")),
  person("Samuel", "Borms", email = "samuel.borms@unine.ch", role = c("aut", "cre")),
  person("Kris", "Boudt", email = "kris.boudt@vub.be", role = c("aut")))
Author: David Ardia [aut],
  Keven Bluteau [aut],
  Samuel Borms [aut, cre],
  Kris Boudt [aut]
Maintainer: Samuel Borms <samuel.borms@unine.ch>
Description: Optimized prediction based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in various ways. See Ardia et al. (2017) <https://ssrn.com/abstract=3067734>.
Depends: R (>= 3.3.0), data.table, ggplot2, foreach
License: GPL (>= 2)
BugReports: https://github.com/sborms/sentometrics/issues
URL: https://github.com/sborms/sentometrics
Encoding: UTF-8
LazyData: true
Suggests: testthat, e1071, randomForest
Imports: utils, stats, quanteda, stringi, zoo, abind, glmnet, caret,
        compiler, Rcpp (>= 0.12.13), RcppRoll, ggthemes, ISOweek, MCS
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-03-26 19:14:14 UTC; gebruiker
Repository: CRAN
Date/Publication: 2018-03-26 19:33:24 UTC
Built: R 3.3.3; x86_64-w64-mingw32; 2018-04-22 18:02:58 UTC; windows
Archs: i386, x64
