Title: Process Nanopore EPI2ME Output for Use in R
Authors@R: c(person("Maggie", "Weinroth", role = c("aut")),
person(c('Quentin', 'D.'), 'Read', email = 'quentin.read@usda.gov', role = c('aut', 'cre')))
Description: The functions in this package take WIMP and ARMA data files generated by Oxford Nanopore EPI2ME workflows,
read them, clean them, and prepare them for downstream analysis.
This package was written by United States federal government employees in their official capacity. Therefore, it is not protected by copyright and is in the public domain.
Imports: data.table, taxonomizr, phyloseq, Biobase, metagenomeSeq
Suggests: rmarkdown, knitr, reshape2, testthat (>= 3.0.0)

Title: Epidemiological Data Display Package
Depends: R (>= 2.6.2), foreign, survival, MASS, nnet
Description: Package for data exploration and result presentation.
Full 'epicalc' package with data management functions is available
Packaged: 2015-07-01 13:18:17 UTC; Virasakdi

Packaged: 2015-11-02 15:32:22 UTC; Virasakdi

Description: Package for data exploration and result presentation. Full 'epicalc' package with data management functions is available at '<http://medipe.psu.ac.th/epicalc>'.
Packaged: 2018-05-10 17:12:26 UTC; Virasakdi's Surface

Description: Package for data exploration and result presentation. Full 'epicalc' package with data management functions is available at '<https://medipe.psu.ac.th/epicalc/>'.
Packaged: 2022-05-18 10:43:00 UTC; Virasakdi

Package: epiGWAS
Title: Robust Methods for Epistasis Detection
Version: 1.0.0
Authors@R: c(person("Lotfi", "Slim", email = "lotfi.slim@mines-paristech.fr", role = c("aut", "cre")),
person("Clément", "Chatelain", email = "clement.chatelain@sanofi.com", role = "ctb"),
person("Chloé-Agathe", "Azencott", email = "chloe-agathe.azencott@mines-paristech.fr", role ="ctb"),
person("Jean-Philippe", "Vert", email = "jpvert@google.com", role ="ctb"))
Description: Functions to perform robust epistasis detection in genome-wide
association studies, as described in Slim et al. (2018) <doi:10.1101/442749>.
The implemented methods identify pairwise interactions between a particular
target variant and the rest of the genotype, using a propensity score
approach. The propensity score models the linkage disequilibrium between the
target and the rest of the genotype. All methods are penalized regression
approaches, which differently incorporate the propensity score to only
recover the synergistic effects between the target and the genotype.
Depends: R (>= 3.4.0)
Imports: matrixStats, DescTools, glmnet, SNPknock
Suggests: foreach, iterators, precrec, parallel, doParallel, bigmemory,
biglasso, testthat, knitr, rmarkdown, kableExtra
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-12-04 20:00:21 UTC; lotfislim
Author: Lotfi Slim [aut, cre],
Clément Chatelain [ctb],
Chloé-Agathe Azencott [ctb],
Jean-Philippe Vert [ctb]
Maintainer: Lotfi Slim <lotfi.slim@mines-paristech.fr>
Repository: CRAN
Date/Publication: 2018-12-16 14:10:03 UTC