Fit model for datasets with easy-to-interpret Gaussian process modeling, predict responses for new inputs.
    The input variables of the datasets can be quantitative, qualitative/categorical or mixed.
    The output variable of the datasets is a scalar (quantitative).
    The optimization of the likelihood function can be chosen by the users (see the documentation of EzGP_fit()).
    The modeling method is published in "EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors" 
    by Qian Xiao, Abhyuday Mandal, C. Devon Lin, and Xinwei Deng (2022) <doi:10.1137/19M1288462>.

An extension of 'knitr' that adds flexibility in several
    ways. One common source of frustration with 'knitr' is that it assumes
    the directory where the source file lives should be the working directory,
    which is often not true. 'ezknitr' addresses this problem by giving you
    complete control over where all the inputs and outputs are, and adds several
    other convenient features to make rendering markdown/HTML documents easier.

Serves as a platform for published fluorometric enzyme assay
 protocols. 'ezmmek' calibrates, calculates, and plots enzyme activities as they
 relate to the transformation of synthetic substrates. At present, 'ezmmek'
 implements two common protocols found in the literature, and is modular to accommodate
 additional protocols. Here, these protocols are referred to as the In-Sample
 Calibration (Hoppe, 1983; <doi:10.3354/meps011299>) and In-Buffer Calibration (German et al., 2011; <doi:10.1016/j.soilbio.2011.03.017>).
 protocols. By containing multiple protocols, 'ezmmek' aims to stimulate
 discussion about how to best optimize fluorometric enzyme assays. A standardized
 approach would make studies more comparable and reproducible.

Wrapper for the 'ggplot2' package that creates a variety of common
  charts (e.g. bar, line, area, ROC, waterfall, pie) while aiming to reduce
  typing.

Runs a Shiny App in the local machine for basic
    statistical and graphical analyses. The point-and-click interface 
    of Shiny App enables obtaining the same analysis outputs (e.g., plots and
    tables) more quickly, as compared with typing the required code in R,
    especially for users without much experience or expertise with coding.
    Examples of possible analyses include tabulating descriptive
    statistics for a variable, creating histograms by experimental groups,
    and creating a scatter plot and calculating the correlation between
    two variables.