Package: sentometrics
Type: Package
Title: An Integrated Framework for Textual Sentiment Time Series
        Aggregation and Prediction
Version: 0.5.6
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. (2018) <doi:10.2139/ssrn.3067734>.
Depends: R (>= 3.3.0), data.table
License: GPL (>= 2)
BugReports: https://github.com/sborms/sentometrics/issues
URL: https://github.com/sborms/sentometrics
Encoding: UTF-8
LazyData: true
Suggests: covr, doParallel, e1071, parallel, randomForest, testthat
Imports: caret, compiler, foreach, ggplot2, glmnet, ISOweek, quanteda,
        Rcpp (>= 0.12.13), RcppRoll, RcppParallel, stats, stringi,
        utils
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
RoxygenNote: 6.1.0
SystemRequirements: GNU make
NeedsCompilation: yes
Packaged: 2018-12-17 19:08:51 UTC; gebruiker
Repository: CRAN
Date/Publication: 2018-12-18 12:10:03 UTC
Built: R 3.4.4; x86_64-w64-mingw32; 2019-04-25 21:49:09 UTC; windows
Archs: i386, x64
