Packaged: Mon Aug 21 10:18:48 2006; Lukke

Title: Explainable Outlier Detection Through Decision Tree Conditioning
Description: Will try to fit decision trees that try to "predict" values for each column based on the values of each other column.
Along the way, each time a split is evaluated, it will take the observations that fall into each branch as a homogeneous
cluster in which it will search for outliers in the 1-d distribution of the column being predicted. Outliers are determined
according to confidence intervals on this 1-d distribution, and need to have a large gap with respect to the next observation
in sorted order to be flagged as outliers. Since outliers are searched for in a decision tree branch, it will know the conditions
that make it a rare observation compared to others that meet the same conditions, and the conditions will always be correlated
with the target variable (as it's being predicted from them). Loosely based on the 'GritBot' <https://www.rulequest.com/gritbot-info.html> software.

according to confidence intervals in this 1-d distribution, and need to have a large gap with respect to the next observation
with the target variable (as it's being predicted from them). Full procedure is described in Cortes (2020) <arXiv:2001.00636>.
Loosely based on the 'GritBot' <https://www.rulequest.com/gritbot-info.html> software.

Description: Outlier detection method that flags suspicious values within observations,
constrasting them against the normal values in a user-readable format, potentially
describing conditions within the data that make a given outlier more rare.
Full procedure is described in Cortes (2020) <arXiv:2001.00636>.

Suggests: knitr, rmarkdown, kableExtra, data.table

Suggests: knitr, rmarkdown, kableExtra, magrittr, dplyr

Description: Create regression tables for publication.
Currently supports 'lm', 'glm', 'survreg', and 'ivreg' outputs.
Imports: magrittr, reshape2, sandwich, stats, stringr, tidyr, utils

Title: Install and Run Programs, Outside of R, Inside of R
Description: Install and run external command-line programs in R through use of
'Docker' <https://www.docker.com/> and online repositories.
Imports: outsider.base, remotes (>= 2.0), crayon, jsonlite, tibble,
Suggests: testthat (>= 2.1), knitr (>= 1.0), ape, rlang, ssh
Author: Dom Bennett [aut, cre, cph] (<https://orcid.org/0000-0003-2722-1359>),