Description: Tools to interface with Cytobank's API via R, organized by various
endpoints that represent various areas of Cytobank functionality. Learn more

person("Preston", "Ng", email="preston@cytobank.org", role=c("aut", "cre")),

Packaged: 2021-09-29 13:54:49 UTC; stefanietrop

person("Qihao", "Qi", email="qqi@beckman.com", role=c("aut", "cre")),
Description: Tools to interface with Cytobank's API via R, organized by
Imports: jsonlite, methods, stats, aws.s3, uuid, jose

person("Angel", "Figueroa", email="afigueroa03@beckman.com", role=c("aut", "cre")),

Title: Computes Signaling and Population Stats for Cytometry Data on
Description: Tools to process cytometry data from Cytobank into easily usable form for analysis of populations, markers, and signaling using the 'CytobankAPI' package. Learn more about Cytobank at <https://www.cytobank.org>. For more information about types of cytometry data that can be analyzed, please see: Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E. D., Krutzik, P. O., Finck, R.,... Nolan, G. P. (2011) <doi:10.1126/science.1198704> and Adan, A., Alizada, G., Kiraz, Y., Baran, Y., Nalbant, A. (2017). <doi:10.3109/07388551.2015.1128876>.
Suggests: httr, methods, curl, stats, jsonlite

Imports: CytobankAPI, shiny, xlsx, shinyFiles, pheatmap
Packaged: 2018-06-27 14:33:51 UTC; allisonthrom

Title: Bridging and Extending the CytobankAPI Package in R to the
person("Katherine", "Drake", email="katherine@cytobank.org ", role="aut"),
Description: A collection of tools that leverage the CytobankAPI R package <https://cran.r-project.org/web/packages/CytobankAPI/vignettes/cytobank-quickstart.html> to complete more complex workflows, and add/extend various Cytobank features.

Package: D2C
Type: Package
Title: Predicting Causal Direction from Dependency Features
Version: 1.0
Date: 2014-06-10
Author: Gianluca Bontempi, Catharina Olsen, Maxime Flauder
Maintainer: Catharina Olsen <colsen@ulb.ac.be>
Description: The relationship between statistical dependency and causality lies
at the heart of all statistical approaches to causal inference. The D2C
package implements a supervised machine learning approach to infer the
existence of a directed causal link between two variables in multivariate
settings with n>2 variables. The approach relies on the asymmetry of some
conditional (in)dependence relations between the members of the Markov
blankets of two variables causally connected. The D2C algorithm predicts
the existence of a direct causal link between two variables in a
multivariate setting by (i) creating a set of of features of the
relationship based on asymmetric descriptors of the multivariate dependency
and (ii) using a classifier to learn a mapping between the features and the
presence of a causal link
License: Artistic-2.0
Depends: randomForest
Imports: gRbase, lazy, RBGL, MASS, corpcor, infotheo, methods,
Rgraphviz
LazyData: true
Packaged: 2014-11-10 14:36:35 UTC; cat
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-11-10 18:07:50