references.bib

@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2018},
    url = {https://www.R-project.org/},
  }

@book{eda,
   address = {Reading, MA},
   author =    {John W. Tukey},
   title =     {Exploratory Data Analysis},
   publisher = {Addison--Wesley},
   year =      1977,
}

@Book{xie2015,
  title = {Dynamic Documents with {R} and knitr},
  author = {Yihui Xie},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2015},
  edition = {2nd},
  note = {ISBN 978-1498716963},
  url = {http://yihui.name/knitr/},
}

@article{hyndman1996,
 ISSN = {00031305},
 URL = {http://www.jstor.org/stable/2684423},
 abstract = {Many statistical methods involve summarizing a probability distribution by a region of the sample space covering a specified probability. One method of selecting such a region is to require it to contain points of relatively high density. Highest density regions are particularly useful for displaying multimodal distributions and, in such cases, may consist of several disjoint subsets-one for each local mode. In this paper, I propose a simple method for computing a highest density region from any given (possibly multivariate) density f(x) that is bounded and continuous in x. Several examples of the use of highest density regions in statistical graphics are also given. A new form of boxplot is proposed based on highest density regions; versions in one and two dimensions are given. Highest density regions in higher dimensions are also discussed and plotted.},
 author = {Rob J. Hyndman},
 journal = {The American Statistician},
 number = {2},
 pages = {120-126},
 publisher = {[American Statistical Association, Taylor & Francis, Ltd.]},
 title = {Computing and Graphing Highest Density Regions},
 volume = {50},
 year = {1996}
}

@article{hyndman1996b,
 ISSN = {10618600},
 URL = {http://www.jstor.org/stable/1390887},
 abstract = {We consider the kernel estimator of conditional density and derive its asymptotic bias, variance, and mean-square error. Optimal bandwidths (with respect to integrated mean-square error) are found and it is shown that the convergence rate of the density estimator is order n<sup>-2/3</sup>. We also note that the conditional mean function obtained from the estimator is equivalent to a kernel smoother. Given the undesirable bias properties of kernel smoothers, we seek a modified conditional density estimator that has mean equivalent to some other nonparametric regression smoother with better bias properties. It is also shown that our modified estimator has smaller mean square error than the standard estimator in some commonly occurring situations. Finally, three graphical methods for visualizing conditional density estimators are discussed and applied to a data set consisting of maximum daily temperatures in Melbourne, Australia.},
 author = {Rob J. Hyndman, David M. Bashtannyk, Gary K. Grunwald},
 journal = {Journal of Computational and Graphical Statistics},
 number = {4},
 pages = {315-336},
 publisher = {[American Statistical Association, Taylor & Francis, Ltd., Institute of Mathematical Statistics, Interface Foundation of America]},
 title = {Estimating and Visualizing Conditional Densities},
 volume = {5},
 year = {1996}
}


@article{mohammad13,
  author = {Mohammad, Saif M. and Turney, Peter D.},
  title = {Crowdsourcing a Word-Emotion Association Lexicon},
  journal = {Computational Intelligence},
  volume = {29},
  number = {3},
  pages = {436-465},
  doi = {10.1111/j.1467-8640.2012.00460.x},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8640.2012.00460.x},
  eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-8640.2012.00460.x},
  year = {2013}
}