Title: Epigenetic Variable Outliers for Risk prediction Analysis
Description: EVORA is an adaptive index classification algorithm which
selects features based on differential variability and is aimed

Title: Mean of order p (MOP) and peaks over random threshold (PORT)
Hill estimates for the extreme value index (EVI)
Maintainer: B G Manjunath <bgmanjunath@gmail.com>
Description: Compute EVI estimate using MOP and PORT methodology.

Title: Mean of order p, peaks over random threshold Hill and high
Author: B G Manjunath and Frederico Caeiro; guidance from Prof. M. Ivette Gomes and Prof. M. Isabel Fraga Alves
Description: Computes extreme value index (EVI) estimate for heavy tailed models by Mean of order p (MOP)
and peaks over random threshold (PORT) Hill methodologies.
Besides, also computes moment, generalised Hill and mixed moment estimates for EVI.
Compute high quantile or value-at-risk (VaR) based on above EVI estimates.

Title: Extreme Value Theory for Open Set Classification - GPD and GEV
Authors@R: person("Edoardo", "Vignotto", email = "edoardo.vignotto@unige.ch", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-9870-8020"))
Description: Two classifiers for open set recognition and novelty detection based on extreme value theory. The first classifier is based on the generalized Pareto distribution (GPD) and the second classifier is based on the generalized extreme value (GEV) distribution. For details, see Vignotto, E., & Engelke, S. (2018) <arXiv:1808.09902>.
Author: Edoardo Vignotto [aut, cre] (<https://orcid.org/0000-0001-9870-8020>)

Package: evtree
Title: Evolutionary Learning of Globally Optimal Trees
Version: 0.1-0
Date: 2011-09-29
Authors@R: c(person(given = "Thomas", family = "Grubinger", role =
c("aut", "cre"), email = "Thomas.Grubinger@i-med.ac.at"),
person(given = "Achim", family = "Zeileis", role = "aut", email
= "Achim.Zeileis@R-project.org"), person(given = "Karl-Peter",
family = "Pfeiffer", role = "aut", email =
"Karl-Peter.Pfeiffer@i-med.ac.at"))
Author: Thomas Grubinger [aut, cre], Achim Zeileis [aut], Karl-Peter
Pfeiffer [aut]
Maintainer: Thomas Grubinger <Thomas.Grubinger@i-med.ac.at>
Description: Commonly used classification and regression tree methods
like the CART algorithm are recursive partitioning methods that
build the model in a forward stepwise search.  Although this
approach is known to be an efficient heuristic, the results of
recursive tree methods are only locally optimal, as splits are
chosen to maximize homogeneity at the next step only. An
alternative way to search over the parameter space of trees is
to use global optimization methods like evolutionary
algorithms. The evtree package implements an evolutionary
algorithm for learning globally optimal classification and
regression trees in R. CPU and memory-intensive tasks are fully
computed in C++ while the partykit package is leveraged to
represent the resulting trees in R, providing unified
infrastructure for summaries, visualizations, and predictions.
Depends: R (>= 2.11.0), partykit
Suggests: mlbench, kernlab, multcomp, rpart, party, xtable
LazyData: yes
License: GPL-2
Packaged: 2011-09-30 12:44:52 UTC; zeileis
Repository: CRAN
Date/Publication: 2011-09-30 15:47:44