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 |
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: psda_1.4.0.tgz, r-oldrel: psda_1.4.0.tgz |
Old sources: | psda archive |
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