Collection of datasets from Sen & Srivastava: "Regression
        Analysis, Theory, Methods and Applications", Springer.  Sources
        for individual data files are more fully documented in the
        book.

Sensitivity analysis in unmatched observational studies, with or without strata.  The main functions are sen2sample() and senstrat().  See Rosenbaum, P. R. and Krieger, A. M. (1990), JASA, 85, 493-498, <doi:10.1080/01621459.1990.10476226> and Gastwirth, Krieger and Rosenbaum (2000), JRSS-B, 62, 545–555 <doi:10.1111/1467-9868.00249> .

Unsupervised text tokenizer allowing to perform byte pair encoding and unigram modelling. 
    Wraps the 'sentencepiece' library <https://github.com/google/sentencepiece> which provides a language independent tokenizer to split text in words and smaller subword units. 
    The techniques are explained in the paper "SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing" by Taku Kudo and John Richardson (2018) <doi:10.18653/v1/D18-2012>.
    Provides as well straightforward access to pretrained byte pair encoding models and subword embeddings trained on Wikipedia using 'word2vec', 
    as described in "BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages" by Benjamin Heinzerling and Michael Strube (2018) <http://www.lrec-conf.org/proceedings/lrec2018/pdf/1049.pdf>.

Sentiment Analysis via deep learning and gradient boosting models with a lot of the underlying hassle taken care of to make the process as simple as possible. 
  In addition to out-performing traditional, lexicon-based sentiment analysis (see <https://benwiseman.github.io/sentiment.ai/#Benchmarks>),
  it also allows the user to create embedding vectors for text which can be used in other analyses.
  GPU acceleration is supported on Windows and Linux.

Performs a sentiment analysis of textual contents in R. This implementation
    utilizes various existing dictionaries, such as Harvard IV, or finance-specific 
    dictionaries. Furthermore, it can also create customized dictionaries. The latter 
    uses LASSO regularization as a statistical approach to select relevant terms based on 
    an exogenous response variable.