An alternative approach to non-standard evaluation using
    formulas. Provides a full implementation of LISP style 'quasiquotation',
    making it easier to generate code with other code.

Provides a framework for lazy computation on large sparse matrices.
    Enables lazy evaluation of normalized data matrices, preserving sparsity
    throughout operations without materializing dense intermediate objects.
    Implements statistical algorithms including LSQR for sparse least squares
    as described in Paige and Saunders (1982)
    <doi:10.1145/355984.355989> and partial singular value
    decomposition via the augmented implicitly restarted Lanczos
    bidiagonalization algorithm of Baglama and Reichel (2005)
    <doi:10.1137/04060593X>.

Lazy read for drawings. A 'dplyr' back end for data sources supported by 
    'GDAL' vector drivers, that allows working with local or remote sources as if they 
    are in-memory data frames. Basic features work with any drawing format ('GDAL vector 
    data source') supported by the 'gdalraster' package.

Helper functions to build SQL statements
    for dbGetQuery or dbSendQuery under program control.
    They are intended to increase speed of coding and
    to reduce coding errors. Arguments are carefully checked,
    in particular SQL identifiers such as names of tables or columns.
    More patterns will be added as required.

Provide sets of functions and methods to learn and practice data science using idea of algorithmic trading.
    Main goal is to process information within "Decision Support System" to come up with analysis or predictions.
    There are several utilities such as dynamic and adaptive risk management using reinforcement learning
    and even functions to generate predictions of price changes using pattern recognition deep regression learning.
    Summary of Methods used: Awesome H2O tutorials: <https://github.com/h2oai/awesome-h2o>, 
    Market Type research of Van Tharp Institute: <https://vantharp.com/>,
    Reinforcement Learning R package: <https://CRAN.R-project.org/package=ReinforcementLearning>.