Package: outliertree
Type: Package
Title: Explainable Outlier Detection Through Decision Tree Conditioning
Version: 1.3.0
Date: 2020-06-07
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera@gmail.com>
URL: https://github.com/david-cortes/outliertree
BugReports: https://github.com/david-cortes/outliertree/issues
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 in 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). Full procedure is described in Cortes (2020) <arXiv:2001.00636>.
	Loosely based on the 'GritBot' <https://www.rulequest.com/gritbot-info.html> software.
License: GPL (>= 3)
Imports: Rcpp (>= 1.0.1)
Depends: R (>= 3.5.0)
LinkingTo: Rcpp, Rcereal
LazyData: true
RoxygenNote: 7.1.0
NeedsCompilation: yes
Packaged: 2020-06-07 15:31:19 UTC; david
Repository: CRAN
Date/Publication: 2020-06-10 15:50:02 UTC
Built: R 4.1.0; x86_64-w64-mingw32; 2020-08-03 05:10:55 UTC; windows
Archs: i386, x64
