Data visualization is important for statistical analysis, as it helps convey information efficiently and shed lights on the hidden patterns behind data in a visual context. It is particularly helpful to display circular data in a two-dimensional space to accommodate its nonlinear support space and reveal the underlying circular structure which is otherwise not obvious in one-dimension. In this article, we first formally categorize circular plots into two types, either height- or area-proportional, and then describe a new general methodology that can be used to produce circular plots, particularly in the area-proportional manner, which in our opinion is the more appropriate choice. Formulas are given that are fairly simple yet effective to produce various circular plots, such as smooth density curves, histograms, rose diagrams, dot plots, and plots for multiclass data. Supplemental materials for this article are available online.

The presented software package is an advanced analogue of the famous HBOOK and, in part, ROOT packages.

The main features of the package are as follows.

Standard operations (accumulation, simulation, transformation) are extended up to 5D objects.

A new type of transformations of objects has been introduced, the x- transformation, which includes convolution of distributions.

Objects are accessed mainly by alphabetical name.

The formation of the object space is carried out using a data file, where the user can choose the form of setting attributes.

Automatic adjustment of object attributes is possible (only the number of channels is set).

The number of channels for each of the axes of the object is unlimited.

Operations between two objects (addition, subtraction, etc.) are possible with mismatched attributes. Only the ranks of the objects should match.

Data output is carried out in two forms, for graphics and for fit.

Group operations are provided for visualization, outputting files for graphics.

In the last version the program for x-converting 2D to 2D has been added.

A group of small programs has been added to simplify the user interface.

Improved representation of 2D objects, while removing the limit on the number of channels for all axes.

A number of bugs are fixed.

All programmes are written in FORTRAN-90.

The investigation has been performed at the Veksler and Baldin Laboratory of High Energy Physics, JINR.

Osma vežba za predmet Tehnike obrade biomedicinskih signala na master akademskim studijama na Elektrotehničkom fakultetu Univerziteta u Beogradu.

In this MANUAL FOR VISIBILITY GRAPHS MODELING USING R-STUDIO We will first present basic notions that will allow the understanding of the mapping process, then we'll show the computational idea. Finally, let's work with the R scripts inside the RStudio, exploring pseudo-random series, Brownian motion series, periodic series, series of fibonacci and series of audio signals. We'll show you: 1) how to generate time series in RS Studio and later turn them into visibility graphs. 2) how to import time series allocated in a directory, turning them into visibility graphs. 3) how to visualize networks using three types of algorithms, followed by calculation and visualization of the main properties of complex networks. About the codes included The 3 codes included generates visibility graphs of series generated by RStudio functions. This code also calculates some metrics for complex networks, generates the graph plot and its degree distribution, shows the plot of the series and its histogram.

This summary encapsulates the step-by-step process of conducting Hierarchical Cluster Analysis (HCA) using OPUS 8.5 software, covering data preparation, analysis execution, and result interpretation.

Software Launch: Open OPUS 8.5 software. Accessing Cluster Analysis: Navigate to the "Evaluate" dropdown menu. Selecting Cluster Analysis: Choose "Cluster Analysis" from the dropdown menu. Loading Method or Default: Load preferred method or proceed with default settings. Navigating to Spectra Reference Tab: Access the "Spectra Reference" tab within the Cluster Analysis interface. Adding Spectra: Import spectral data files into the software. Selecting Spectra: Choose specific spectra files for analysis. Preprocessing (If necessary): Apply preprocessing techniques such as Vector Normalization. Defining Analysis Region: Specify spectral region range for analysis. Initiating Cluster Analysis: Click "Analysis Cluster" to start the analysis process. Reviewing Analysis Report: Access and view the generated cluster analysis report. Exploring Analysis Reports: Explore different report formats (Dendritic, Histogram, Basic Data) for insights. Data Visualization: Utilize visualization tools for further documentation and reference. This summary encapsulates the step-by-step process of conducting Hierarchical Cluster Analysis (HCA) using OPUS 8.5 software, covering data preparation, analysis execution, and result interpretation.