An implementation of symbolic polygonal data analysis. The package presents the estimation of main descriptive statistical measures, e.g, mean, covariance, variance, correlation and coefficient of variation.
In addition, a method to obtain polygonal data from classical data is presented. Empirical probability distribution function based on
polygonal histogram and a regression model with its main measures are presented.

A toolbox in symbolic data framework as a statistical learning and data mining solution for symbolic polygonal data analysis. This study is a new approach in data analysis and it was proposed by
Silva et al. (2019) <doi:10.1016/j.knosys.2018.08.009>. The package presents the estimation of main descriptive statistical measures, e.g, mean, covariance, variance, correlation and coefficient of variation.
In addition, a method to obtain polygonal data from classical data is presented. Empirical probability distribution function based on symbolic polygonal histogram and a regression model with its main measures are presented.

Use Time Series to Generate and Compare Power Spectral Density
person(given = "Yong-Han Hank",
family = "Cheng",
role = c("aut", "cre"),
email = "yhhc@uw.edu",
comment = c(ORCID = "0000-0001-7686-0697"))
Functions that allow you to generate and compare power spectral density (PSD)
plots given time series data. Fast Fourier Transform (FFT) is used to take a time series
data, analyze the oscillations, and then output the frequencies of these oscillations
in the time series in the form of a PSD plot.Thus given a time series, the dominant
frequencies in the time series can be identified. Additional functions in this package
allow the dominant frequencies of multiple groups of time series to be compared with each other.
To see example usage with the main functions of this package, please visit
this site: <https://yhhc2.github.io/psdr/articles/Introduction.html>.
The mathematical operations used to generate the PSDs are described in these sites:
<https://www.mathworks.com/help/matlab/ref/fft.html>.
<https://www.mathworks.com/help/signal/ug/power-spectral-density-estimates-using-fft.html>.
Yong-Han Hank Cheng [aut, cre]
(<https://orcid.org/0000-0001-7686-0697>)