Perform biomarker evaluation and comparison in terms of specificity at a controlled sensitivity level, or sensitivity at a controlled specificity level. Point estimation and exact bootstrap of Huang, Parakati, Patil, and Sanda (2023) <doi:10.5705/ss.202021.0020> for the one- and two-biomarker problems are implemented.

Provides methods for sensory discrimination methods;
  duotrio, tetrad, triangle, 2-AFC, 3-AFC, A-not A, same-different,
  2-AC and degree-of-difference.
  This enables the calculation of d-primes, standard errors of
  d-primes, sample size and power computations, and
  comparisons of different d-primes. Methods for profile likelihood
  confidence intervals and plotting are included. Most methods are 
  described in Brockhoff, P.B. and Christensen, R.H.B. (2010) 
  <doi:10.1016/j.foodqual.2009.04.003>.

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>.