Description: Performs robust cluster analysis allowing for outliers and noise that cannot be fitted by any cluster. The data are modelled by a mixture of Gaussian distributions and a noise component, which is an improper uniform  distribution covering the whole Euclidean space. Parameters are estimated by  (pseudo) maximum likelihood. This is fitted by a EM-type algorithm. See Coretto and Hennig (2016) <doi:10.1080/01621459.2015.1100996>, and Coretto and Hennig (2017) <https://jmlr.org/papers/v18/16-382.html>.
role = c("aut", "cre"), email = "pcoretto@unisa.it"),

Imports: stats, utils, graphics, grDevices, mvtnorm, parallel, foreach,
Author: Pietro Coretto [aut, cre] (Homepage:

Title: Online Time Series Anomaly Detectors
person("Alaiñe", "Iturria", email = "aiturria@ikerlan.es", role = c("aut","cre")),
person("Jacinto", "Carrasco", email = "jacintocc@decsai.ugr.es", role = c("aut")),
person("Francisco", "Herrera", email = "herrera@decsai.ugr.es", role = c("aut")),
person("Santiago", "Charramendieta", email = "scharramendieta@ikerlan.es", role = c("aut")),
person("Karmele", "Intxausti", email = "kintxausti@ikerlan.es", role = c("aut")))
Description: Implements a set of online fault detectors for time-series, called: PEWMA see M. Carter
et al. (2012) <doi:10.1109/SSP.2012.6319708>, SD-EWMA and TSSD-EWMA see H. Raza et al.
<arXiv:1608.04585>, KNN-LDCD see V. Ishimtsev et al. (2017) <arXiv:1706.03412> and
CAD-OSE see M. Smirnov (2018) <https://github.com/smirmik/CAD>. The first three
algorithms belong to prediction-based techniques and the last three belong to
window-based techniques. In addition, the SD-EWMA and PEWMA algorithms are algorithms
designed to work in stationary environments, while the other four
are algorithms designed to work in non-stationary environments.
SystemRequirements: Python (>= 3.0.1); bencode-python3 (1.0.2)
Suggests: testthat, stream, knitr, rmarkdown
Imports: stats, ggplot2, plotly, sigmoid, reticulate

Title: An R Autograding Extension for Otter-Grader
person(given = "UC Berkeley Data Science Education Program",
An R autograding extension for Otter-Grader (<https://otter-grader.readthedocs.io>). It supports
grading R scripts, RMarkdown documents, and R Jupyter Notebooks.
Imports: yaml, testthat, jsonlite, pryr, R6, zip
Packaged: 2022-02-04 19:25:34 UTC; chrispyles
Author: Christopher Pyles [aut, cre] (<https://orcid.org/0000-0001-8520-7593>),
UC Berkeley Data Science Education Program [cph]