It implements an improved and computationally faster version 
            of the original Stepwise Gaussian Graphical Algorithm for estimating 
            the Omega precision matrix from high-dimensional data. 
            Zamar, R., Ruiz, M., Lafit, G. and Nogales, J. (2021) 
            <doi:10.52933/jdssv.v1i2.11>.

An interface to the 'fastText' <https://github.com/facebookresearch/fastText> library for efficient learning of word representations and sentence classification. The 'fastText' algorithm is explained in detail in (i) "Enriching Word Vectors with subword Information", Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomas Mikolov, 2017, <doi:10.1162/tacl_a_00051>; (ii) "Bag of Tricks for Efficient Text Classification", Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov, 2017, <doi:10.18653/v1/e17-2068>; (iii) "FastText.zip: Compressing text classification models", Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Herve Jegou, Tomas Mikolov, 2016, <arXiv:1612.03651>.

An interface to the 'fastText' library
	<https://github.com/facebookresearch/fastText>. The package
	can be used for text classification and to learn word vectors.
	An example how to use 'fastTextR' can be found in the 'README' file.

Fast functions for timestamp
	manipulation that avoid system calls and take shortcuts
	to facilitate operations on very large data.

Implements fast, scalable optimization algorithms for
    fitting topic models ("grade of membership" models) and
    non-negative matrix factorizations to count data. The methods
    exploit the special relationship between the multinomial topic
    model (also, "probabilistic latent semantic indexing") and Poisson
    non-negative matrix factorization. The package provides tools to
    compare, annotate and visualize model fits, including functions to
    efficiently create "structure plots" and identify key features in
    topics. The 'fastTopics' package is a successor to the
    'CountClust' package. For more information, see
    <doi:10.48550/arXiv.2105.13440> and
    <doi:10.1186/s13059-023-03067-9>. Please also see the GitHub
    repository for additional vignettes not included in the package on
    CRAN.