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).

Datasets and functions to benchmark (convergence, speed, ease of use) R packages dedicated to regression with neural networks (no classification in this version). The templates for the tested packages are available in the R, R Markdown and HTML formats at <https://github.com/pkR-pkR/NNbenchmarkTemplates> and <https://theairbend3r.github.io/NNbenchmarkWeb/index.html>. The submitted article to the R-Journal can be read at <https://www.inmodelia.com/gsoc2020.html>.