Leland Wilkinson's Algorithm for Detecting Multidimensional
Outliers
c(person("Leland", "Wilkinson", role = c("aut"),
email = "leland.wilkinson@gmail.com"),
person("Chris", "Fraley", role = c("cre"), email = "cfraley@tableau.com"))
Leland Wilkinson [aut], Chris Fraley [cre]
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>.

c(person("Chris", "Fraley", role = c("aut", "cre"),
email = "cfraley@tableau.com"),
person("Leland", "Wilkinson", role = "ctb",
email = "leland.wilkinson@gmail.com"))
Chris Fraley [aut, cre],
Leland Wilkinson [ctb]

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.

c(person("Chris", "Fraley", role = c("aut", "cre"),
email = "fraley@u.washington.edu"),
person("Leland", "Wilkinson", role = "ctb",
email = "leland.wilkinson@gmail.com"))

High Dimensional Hypothesis Testing for Mean Vectors, Covariance
Matrices, and White Noise of Vector Time Series
High dimensional testing procedures on mean, covariance and white noises.
checkmate (>= 1.6.0), MASS, stats, mvtnorm, foreach,
doParallel, expm

checkmate (>= 1.6.0), MASS, stats, mvtnorm, foreach,
doParallel, expm, fastclime, clime

The Lasso for the Tweedie's Compound Poisson Model Using an
IRLS-BMD Algorithm
Wei Qian <weiqian@stat.umn.edu>, Yi Yang <yiyang@umn.edu>, Hui Zou <hzou@stat.umn.edu>
This package implements a iteratively reweighed least square (IRLS) strategy that incorporates a blockwise majorization decent (BMD) method, for efficiently computing the solution paths of the (grouped) lasso and the (grouped) elastic net for the Tweedie model.