Graphs the pdf or pmf and highlights what area or probability is
    present in user defined locations. Visualize is able to provide lower tail,
    bounded, upper tail, and two tail calculations. Supports strict and equal
    to inequalities.  Also provided on the graph is the mean and variance of
    the distribution.

Visualize the trends and historical downloads from packages in the 'CRAN' repository. Data is obtained by using the 'API' to query the database from the 'RStudio' 'CRAN' mirror.

To visualize the probabilities of early termination,
       fail and success of Simon's two-stage design.  To evaluate
       and visualize the operating characteristics of Simon's
       two-stage design.

Visual contour and 2D point and contour plots for binary classification modeling under algorithms such as 'glm', 'rf', 'gbm', 'nnet' and 'svm', presented over two dimensions generated by 'famd' and 'mca' methods. Package 'FactoMineR' for multivariate reduction functions and package 'MBA' for interpolation functions are used. The package can be used to visualize the discriminant power of input variables and algorithmic modeling, explore outliers, compare algorithm behaviour, etc. It has been created initially for teaching purposes, but it has also many practical uses under the 'XAI' paradigm.

Visualize Variance is an intuitive 'shiny' applications tailored for agricultural research data analysis, including one-way and two-way analysis of variance, correlation, and other essential statistical tools. Users can easily upload their datasets, perform analyses, and download the results as a well-formatted document, streamlining the process of data analysis and reporting in agricultural research.The experimental design methods are based on classical work by Fisher (1925) and Scheffe (1959). The correlation visualization approaches follow methods developed by Wei & Simko (2021) and Friendly (2002) <doi:10.1198/000313002533>.

Visualizes vowel variation in f0, F1, F2, F3 and duration.

A Shiny application and functions for visual exploration of hierarchical clustering with numeric datasets. Allows users to iterative set hyperparameters, select features and evaluate results through various plots and computation of evaluation criteria.