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://www.vantharp.com/>,
    Reinforcement Learning R package: <https://CRAN.R-project.org/package=ReinforcementLearning>.

Provides the functionality to write LaTeX code from within R
    without having to learn LaTeX.  Functionality also exists to create HTML
    and Markdown code.  While the functionality still exists to write
    complete documents with lazyWeave, it is generally easier to do so with
    with markdown and knitr.  lazyWeave's main strength now is the ability
    to design custom and complex tables for reporting results.

Latent budget analysis is a method for the analysis of a two-way
    contingency table with an exploratory variable and a response variable. It is
    specially designed for compositional data.

A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.