Packaged: 2021-03-25 12:05:24 UTC; Paula Saavedra

Description: We provide an R tool for computation and nonparametric plug-in estimation of Highest Density Regions (HDRs) and general level sets in the directional setting. Concretely, circular and spherical HDRs can be reconstructed from a data sample following Saavedra-Nieves and Crujeiras (2021) <doi:10.1007/s11634-021-00457-4>. This library also contains two real datasets in the circular and spherical settings. The first one concerns a problem from animal orientation studies and the second one is related to earthquakes occurrences.
Suggests: ggplot2, maps, mapproj, DirStats
Packaged: 2022-08-06 13:36:54 UTC; Paula Saavedra

Packaged: 2022-11-16 11:06:25 UTC; Paula Saavedra

Packaged: 2021-01-28 10:12:56 UTC; Paula Saavedra

Title: Leland Wilkinson's Algorithm for Detecting Multidimensional
Authors@R: c(person("Leland", "Wilkinson", role = c("aut"),
person("Chris", "Fraley", role = c("cre"), email = "cfraley@tableau.com"))
Author: Leland Wilkinson [aut], Chris Fraley [cre]
Description: An implementation of an algorithm for outlier detection that can handle a) data with a mixed categorical and continuous variables, b) many columns of data, c) many rows of data, d) outliers that mask other outliers, and e) both unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers, HDoutliers is based on a distributional model that uses probabilities to determine outliers. See <https://www.cs.uic.edu/~wilkinson/Publications/outliers.pdf>.

Authors@R: c(person("Chris", "Fraley", role = c("aut", "cre"),
person("Leland", "Wilkinson", role = "ctb",

Depends: R (>= 3.1.0), FNN, FactoMineR, mclust

Description: An implementation of an algorithm for outlier detection that can handle a) data with a mixed categorical and continuous variables, b) many columns of data, c) many rows of data, d) outliers that mask other outliers, and e) both unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers, HDoutliers is based on a distributional model that uses probabilities to determine outliers.