Title: Learn Computer and Data Science using Algorithmic Trading
Description: 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.
Imports: dplyr, lubridate, tidyverse, h2o, ReinforcementLearning,

Imports: readr, stringr, dplyr, lubridate, magrittr, ggplot2,

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>.
Imports: readr, stringr, dplyr, lubridate, ggplot2, grDevices, h2o,
Suggests: testthat (>= 2.1.0), covr, magrittr

Imports: tidyverse, dplyr, lubridate, magrittr, h2o,

Imports: readr, stringr, dplyr, lubridate, magrittr, h2o,

Suggests: testthat (>= 2.1.0), covr, magrittr, data.table, bit64

ReinforcementLearning, openssl, stats, cluster, lifecycle

Imports: readr, stringr, dplyr, tibble, lubridate, ggplot2, grDevices,
h2o, ReinforcementLearning, openssl, stats, cluster, lifecycle

Title: Latent Budget Analysis for Compositional Data
Author: Enio G. Jelihovschi <eniojelihovs@gmail.com>
Maintainer: Enio G. Jelihovschi <eniojelihovs@gmail.com>
Depends: R (>= 3.1.2), MASS, alabama, plotrix, ca
Description: 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.

Author: Enio G. Jelihovschi <eniojelihovs@gmail.com>  and

Depends: R (>= 3.1.0), MASS, alabama, plotrix, scatterplot3d, rgl
Description: 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.