Implements a James-Stein-type shrinkage estimator for 
  the covariance matrix, with separate shrinkage for variances and correlations.  
  The details of the method are explained in Schafer and Strimmer (2005) 
  <DOI:10.2202/1544-6115.1175> and Opgen-Rhein and Strimmer (2007) 
  <DOI:10.2202/1544-6115.1252>.  The approach is both computationally as well
  as statistically very efficient, it is applicable to "small n, large p" data, 
  and always returns a positive definite and well-conditioned covariance matrix.  
  In addition to inferring the covariance matrix the package also provides 
  shrinkage estimators for partial correlations and partial variances.  
  The inverse of the covariance and correlation matrix 
  can be efficiently computed, as well as any arbitrary power of the 
  shrinkage correlation matrix.  Furthermore, functions are available for fast 
  singular value decomposition, for computing the pseudoinverse, and for 
  checking the rank and positive definiteness of a matrix.

Utility functions for the statistical analysis of corpus frequency data.
        This package is a companion to the open-source course "Statistical Inference: 
        A Gentle Introduction for Computational Linguists and Similar Creatures" ('SIGIL').

Facilitates dynamic exploration of text collections through an
    intuitive graphical user interface and the power of regular expressions.
    The package contains 1) a helper function to convert a data frame to a
    'corporaexplorerobject', 2) a 'Shiny' app for fast and flexible exploration
    of a 'corporaexplorerobject', and 3) a 'Shiny' app for simple
    retrieval/extraction of documents from a 'corporaexplorerobject' in a
    reading-friendly format. The package also includes demo apps with which
    one can explore Jane Austen's novels and the State of the Union Addresses
    (data from the 'janeaustenr' and 'sotu' packages respectively).

Provides text analysis in R, focusing on the use of a tokenized text format. In this format, the positions of tokens are maintained, and each token can be annotated (e.g., part-of-speech tags, dependency relations).
    Prominent features include advanced Lucene-like querying for specific tokens or contexts (e.g., documents, sentences),
    similarity statistics for words and documents, exporting to DTM for compatibility with many text analysis packages,
    and the possibility to reconstruct original text from tokens to facilitate interpretation.