psda: Polygonal Symbolic Data Analysis

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.

Version: 1.4.0
Depends: R (≥ 3.1)
Imports: ggplot2, rgeos, plyr, sp, raster, stats
Suggests: testthat, knitr, rmarkdown
Published: 2020-05-24
Author: Wagner Silva [aut, cre, ths], Renata Souza [aut], Francisco Cysneiros [aut]
Maintainer: Wagner Silva <wjfs at cin.ufpe.br>
BugReports: https://github.com/wagnerjorge/psda/issues
License: GPL-2
URL: https://github.com/wagnerjorge/psda
NeedsCompilation: no
Citation: psda citation info
Materials: README
CRAN checks: psda results

Downloads:

Reference manual: psda.pdf
Vignettes: introdution-psda
Package source: psda_1.4.0.tar.gz
Windows binaries: r-devel: psda_1.4.0.zip, r-release: psda_1.4.0.zip, r-oldrel: psda_1.4.0.zip
macOS binaries: r-release (arm64): psda_1.4.0.tgz, r-release (x86_64): psda_1.4.0.tgz, r-oldrel: psda_1.4.0.tgz
Old sources: psda archive

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