R6, MortalityTables, objectProperties, lubridate, openxlsx,
dplyr, scales, abind, stringr, methods, rlang, kableExtra,
pander, tidyr
knitr, rmarkdown, magrittr, tibble, testthat

R6, MortalityTables, objectProperties, lubridate, openxlsx,
dplyr, scales, abind, stringr, methods, rlang, rmarkdown,
kableExtra, pander, tidyr

A package to implement HMD model life table system
Functions supplied in this package will implement
discriminant analysis to select an appropriate life table
family, select an appropriate alpha level based on a desired
life expectancy at birth, produce a model mortality pattern
based on family and level as well as plot the results.

Two-Parameter HMD Model Life Table System

Tools for Communication with Lightning-Viz Server
The purpose of this package is to enable usage of lightningviz server to be accessible from R. The
server itself can be found at http://lightning-viz.org/ and is required to work with this package. Package
by itself cannot and will not create any visualizations.

For Implementation of Feed Reduction, Learning Examples and Code
Management
This is for code management functions, a Monty Hall simulator, and for implementing my own variable reduction technique called Feed Reduction <http://wbbpredictions.com/wp-content/uploads/2018/12/Redditbot_Paper.pdf>. The Feed Reduction technique is not yet published, but is merely a tool for implementing a series of binary neural networks meant for reducing data into N dimensions, where N is the number of possible values of the response variable.
FNN, beepr, ggplot2, reshape2, keras, dplyr, e1071

For Implementation of Feed Reduction, Learning Examples, NLP and
Code Management
This is for code management functions, NLP tools, a Monty Hall simulator, and for implementing my own variable reduction technique called Feed Reduction <http://wbbpredictions.com/wp-content/uploads/2018/12/Redditbot_Paper.pdf>. The Feed Reduction technique is not yet published, but is merely a tool for implementing a series of binary neural networks meant for reducing data into N dimensions, where N is the number of possible values of the response variable.
FNN, beepr, ggplot2, keras, dplyr, e1071, readr, parallel,
textclean, tidyr, forcats, tm, fastmatch, data.table, SnowballC