Creating efficiently new column(s) in a data frame (including tibble) by applying a function one row at a time.

Simplifies the whole process of creating stacked tilted maps,
    that are often used in scientific publications to show different
    environmental layers for a geographical region. Tilting maps and
    layering them allows to easily draw visual correlations between these
    environmental layers.

A simple progress bar showing estimated remaining time. 
    Multiple forecast methods and user defined forecast method for 
    the remaining time are supported.

Supplies a LazyData facility for packages which have data
		sets but do not provide LazyData: true.  A single function is
		is included, requireData, which is a drop-in replacement for
		base::require, but carrying the additional
		functionality. By default, it suppresses package
		startup messages as well.  See argument 'reallyQuitely'.

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.

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 works with any drawing format ('GDAL vector 
    data source') supported by the 'sf' 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>.