c(person("Clara", "Bicalho", email = "clarabmcorreia@gmail.com", role = c("ctb")),
person("Jasper", "Cooper", email = "jjc2247@columbia.edu", role = c("ctb")),
person("Macartan", "Humphreys", email = "mh2245@columbia.edu", role = c("aut")),
person("Till", "Tietz", email = "ttietz2014@gmail.com", role = c("aut","cre")),
person("Alan", "Jacobs", email = "alan.jacobs@ubc.ca", role = c("aut")),
person("Merlin", "Heidemanns", email = "mnh2123@columbia.edu ", role = c("ctb")),
person("Lily", "Medina", email = "lilymiru@gmail.com", role = c("aut")),
person("Julio", "Solis", email = "juliosolisar@gmail.com", role = c("ctb")),
person("Georgiy", "Syunyaev", email = "gs2716@columbia.edu", role = c("ctb")))
Clara Bicalho [ctb],
Jasper Cooper [ctb],
Macartan Humphreys [aut],
Till Tietz [aut, cre],
Alan Jacobs [aut],
Merlin Heidemanns [ctb],
Lily Medina [aut],
Julio Solis [ctb],
Georgiy Syunyaev [ctb]

Analysis utilities for binary classification and Causata users.
Justin Hemann, David Barker, Suzanne Weller, Jason McFall
The Causata package provides utilities for extracting data
from the Causata application, training binary classification
models, and exporting models as PMML for scoring.
XML, R.utils, rjson, RMySQL, RCurl, stringr, yaml, boot,
foreach, data.table, glmnet, ggplot2

The Causata package provides utilities for
extracting data from the Causata application, training binary classification
models, and exporting models as PMML for scoring.

Control Charts with Guaranteed In-Control Performance and
Cautious Parameters Learning
Design and use of control charts for detecting mean changes
based on a delayed updating of the in-control parameter estimates.
See Capizzi and Masarotto (2019) <doi:10.1080/00224065.2019.1640096> for the
description of the method.

Use pathway topology information to assign weight to
pathway nodes.

igraph (>= 0.6), stats, graphics, methods, grDevices,
parallel, Rgraphviz, graph

Use pathway topology information to assign weight to pathway nodes.

person("Zuguang", "Gu", email = "z.gu@dkfz.de", role = c("aut", "cre"),
comment = c('ORCID'="0000-0002-7395-8709"))
It aims to find significant pathways through
network topology information. It has several advantages compared
with current pathway enrichment tools. First, pathway node instead
of single gene is taken as the basic unit when analysing networks
to meet the fact that genes must be constructed into complexes to
hold normal functions. Second, multiple network centrality measures are
applied simultaneously to measure importance of nodes from different
aspects to make a full view on the biological system. CePa extends
standard pathway enrichment methods, which include both over-representation
analysis procedure and gene-set analysis procedure.
<doi:10.1093/bioinformatics/btt008>.