FNN, stringi, beepr, ggplot2, keras, dplyr, readr, parallel,
textclean, tm, e1071, SnowballC, data.table, fastmatch,
neuralnet

This is for code management functions, NLP tools, a Monty Hall simulator, and for implementing my own variable reduction technique called Feed Reduction. 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, stringi, beepr, ggplot2, keras, dplyr, readr, parallel,
tm, e1071, SnowballC, data.table, fastmatch, neuralnet

Palettes generated from limnology based field and laboratory photos. Palettes can be used to generate color values to be used in any functions that calls for a color (i.e. ggplot(), plot(), flextable(), etc.).
c(person("Paul", "Julian", role = c("aut", "cre"),
email = "pauljulianphd@gmail.com", comment = c(ORCID = "0000-0002-7617-1354")))

Static Univariate Frequentist and Bayesian Linear Calibration
Derick L. Rivers <riversdl@alumni.vcu.edu> and Edward L. Boone
Estimate and confidence/credible intervals for an unknown
regressor x0 given an observed y0.

Derick L. Rivers <riversdl@vcu.edu> and Edward L. Boone

Interactive Interpretation of Linear Regression Models
Interactive visualization of effects and marginal effects for
linear regression models and of link functions, response functions and
marginal effects for generalized linear regression models with binary
dependent variables. A major feature is the handling of the effects of
categorical covariates: if two or more factors are used as covariates every
combination of the levels of each factor is treated separately. The
interactive approach, the automatic calculation of marginal effects and
far-reaching possibilities to customize the graphical output are
characteristic features as well.

Interactive visualization of effects, response functions
and marginal effects for different kinds of regression models. In this version
linear regression models, generalized linear models, generalized additive
models and linear mixed-effects models are supported.
Major features are the interactive approach and the handling of the effects of categorical covariates:
if two or more factors are used as covariates every combination of the levels of each
factor is treated separately. The automatic calculation of
marginal effects and a number of possibilities to customize the graphical output
are useful features as well.