URL: https://github.com/abarbour/psd, Barbour and Parker (2014):

Author: Andrew J. Barbour [aut, cre] (<https://orcid.org/0000-0001-6473-5493>),

Title: Adaptive, Sine-Multitaper Power Spectral Density and Cross
at each frequency, but modified for computational stability. The same
procedure can now be used to calculate the cross spectrum.
Imports: Rcpp (>= 0.11.5), RColorBrewer, zoo
Suggests: bspec, fftw (>= 1.0.3), ggplot2 (>= 0.9), multitaper, plyr,
RSEIS, rbenchmark, reshape2, knitr, rmarkdown, signal, formatR,

procedure can now be used to calculate the cross spectrum (multivariate

Authors@R: c(person("Wagner", "Silva", email = "wjfs@cin.ufpe.br", role = c("aut", "cre", "ths")),
person("Renata", "Souza", email = "rmcrs@cin.ufpe.br", role = c("aut")),
person("Francisco", "Cysneiros", email = "cysneiros@de.ufpe.br", role = c("aut")))
Description: 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, transformation of the data in polygons. Empirical probability distribution function based on
polygonal histogram and regression models are presented.
Imports: ggplot2, rgeos, plyr, sp, raster, stats

Packaged: 2018-10-06 13:40:20 UTC; Wagner Jorge

Packaged: 2018-12-15 15:26:29 UTC; Wagner Jorge

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.
Packaged: 2019-03-19 13:34:40 UTC; Wagner Jorge

Packaged: 2019-03-20 04:31:18 UTC; Wagner Jorge

Description: 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.
Packaged: 2019-06-24 15:30:19 UTC; Wagner Jorge