person("Kim", "Seonghyun", , "shyeon.kim@scipi.net", c("aut", "cre"))

Title: Continuous Analog of a Binomial Distribution
Description: Implementation of the d/p/q/r family of functions for a continuous analog to the standard discrete binomial with continuous size parameter and continuous support with x in [0, size + 1], following Ilienko (2013) <arXiv:1303.5990>.
Packaged: 2018-04-10 15:26:19 UTC; ddalthorp

Packaged: 2018-06-10 06:17:05 UTC; ddalthorp

Packaged: 2018-06-22 04:48:19 UTC; ddalthorp

Packaged: 2018-06-26 23:12:20 UTC; ddalthorp

Packaged: 2020-11-22 05:21:19 UTC; ddalthorp

Title: Browse and Query Clinical and Genomic Data from cBioPortal
Description: Provides R users with direct access to genomic and clinical data from the 'cBioPortal' web resource via user-friendly
functions that wrap 'cBioPortal's' existing API endpoints <https://www.cbioportal.org/api/swagger-ui/index.html>. Users can browse and query genomic data on mutations,
copy number alterations and fusions, as well as data on tumor mutational burden ('TMB'),
microsatellite instability status ('MSI'), 'FACETS' and select clinical data points (depending on the study).
See <https://www.cbioportal.org/> and Gao et al., (2013) <doi:10.1126/scisignal.2004088> for more information on the cBioPortal web resource.
Suggests: testthat (>= 3.1.4), knitr (>= 1.39), rmarkdown (>= 2.14),
Author: Karissa Whiting [aut, cre] (<https://orcid.org/0000-0002-4683-1868>)

Title: Clustering of Multivariate Binary Data with Dimension Reduction
Description: The clustering of binary data with reducing the dimensionality (CLUSBIRD) proposed by Yamamoto and Hayashi (2015) <doi:10.1016/j.patcog.2015.05.026>.

Title: Causal Discovery under a Confounder Blanket
person("David", "Watson", , "david.s.watson11@gmail.com", role = c("aut", "cre"),
Description: Methods for learning causal relationships among a set of
foreground variables X based on signals from a (potentially much
larger) set of background variables Z, which are known non-descendants
of X. The confounder blanket learner (CBL) uses sparse regression
techniques to simultaneously perform many conditional independence
tests, with complementary pairs stability selection to guarantee
finite sample error control. CBL is sound and complete with respect to
a so-called "lazy oracle", and works with both linear and nonlinear
systems. For details, see Watson & Silva (2022) <arXiv:2205.05715>.
Imports: data.table, foreach, glmnet, lightgbm
Author: David Watson [aut, cre] (<https://orcid.org/0000-0001-9632-2159>)