Due to 'Rstudio's' status as open source software, we believe it will be utilized frequently for future data analysis by users whom lack formal training or experience with 'R'. The 'NMVANOVA' (Novice Model Variation ANOVA) a streamlined variation of experimental design functions that allows novice 'Rstudio' users to perform different model variations one-way analysis of variance without downloading  multiple libraries or packages. Users can easily manipulate the data block, and needed inputs so that users only have to plugin the four designed variables/values.

This shows how NONMEM(R) software works. NONMEM's classical estimation methods like 'First Order(FO) approximation', 'First Order Conditional Estimation(FOCE)', and 'Laplacian approximation' are explained.

Implements a method that builds the coefficients of a polynomial
    model that performs almost equivalently as a given neural network
    (densely connected). This is achieved using Taylor expansion at the
    activation functions.  The obtained polynomial coefficients can be used
    to explain features (and their interactions) importance  in the neural network,
    therefore working as a tool for interpretability or eXplainable Artificial 
    Intelligence (XAI). See Morala et al. 2021 <doi:10.1016/j.neunet.2021.04.036>,
    and 2023 <doi:10.1109/TNNLS.2023.3330328>.

Calculates spatial pattern analysis using a T-square sample procedure.
  This method is based on two measures "x" and "y".
  "x" - Distance from the random point to the nearest individual.
  "y" - Distance from individual to its nearest neighbor.
  This is a methodology commonly used in phytosociology or marine benthos ecology to analyze the species' distribution (random, uniform or clumped patterns).
  Ludwig & Reynolds (1988, ISBN:0471832359).

Provides nearest-neighbors matching and 
    analysis of case-control data. Cui, Z., Marder, E. P., Click, E. S., 
    Hoekstra, R. M., & Bruce, B. B. (2022) <doi:10.1097/EDE.0000000000001504>.

Generates 'LaTeX' code for drawing well-formatted neural network diagrams with 'TikZ'. Users have to define number of neurons on each layer, and optionally define neuron connections they would like to keep or omit, layers they consider to be oversized and neurons they would like to draw with lighter color. They can also specify the title of diagram, color, opacity of figure, labels of layers, input and output neurons. In addition, this package helps to produce 'LaTeX' code for drawing activation functions which are crucial in neural network analysis. To make the code work in a 'LaTeX' editor, users need to install and import some 'TeX' packages including 'TikZ' in the setting of 'TeX' file.