person("Yevhen", "Tupikov", email = "yzt116@psu.edu", role = c("aut", "cre")),
person("Jia", "Li", email = "jiali@psu.edu", role = "aut"))
Imports: Rcpp (>= 0.12.16), RcppProgress (>= 0.1)

Title: Detect Change Points in Means of High Dimensional Data
Description: Objective: Implement new methods for detecting change points in high-dimensional time series data. These new methods can be applied to non-Gaussian data, account for spatial and temporal dependence, and detect a wide variety of change-point configurations, including changes near the boundary and changes in close proximity. Additionally, this package helps address the “small n, large p” problem, which occurs in many research contexts. This problem arises when a dataset contains changes that are visually evident but do not rise to the level of statistical significance due to the small number of observations and large number of parameters. The problem is overcome by treating the dimensions as a whole and scaling the test statistics only by its standard deviation, rather than scaling each dimension individually. Due to the computational complexity of the functions, the package runs best on datasets with a relatively large number of attributes but no more than a few hundred observations.

Title: Directional Highest Density Regions
Authors@R: c(person("Paula", "Saavedra-Nieves", role = c("aut", "cre"),
person("Rosa M", "Crujeiras", role ="aut"),
person("Andrés", "Prieto", role = "ctb"),
person("Felicita", "Scapini", role = "dtc"))
Description: We provide an R tool for nonparametric plug-in estimation of Highest Density Regions (HDRs) in the directional setting. Concretely, circular and spherical regions can be reconstructed from a data sample following Saavedra-Nieves and Crujeiras (2020) <arXiv:2009.08915>. 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.
Imports: NPCirc, circular, rgl, Directional, movMF, graphics, stats,
Packaged: 2020-12-04 18:02:46 UTC; Paula Saavedra
Author: Paula Saavedra-Nieves [aut, cre],