bioc-issue-bot
commented
6 years ago Hi @nhejazi
Thanks for submitting your package. We are taking a quick look at it and you will hear back from us soon.
The DESCRIPTION file for this package is:
Package: methyvim
Title: Differential Methylation Analysis with Targeted Minimum Loss-Based
Estimates of Variable Importance Measures
Version: 0.2.3
Authors@R: c(
person("Nima", "Hejazi", email = "nhejazi@berkeley.edu",
role = c("aut", "cre", "cph")),
person("Rachael", "Phillips", email = "rachaelvphillips@berkeley.edu",
role = "ctb"),
person("Alan", "Hubbard", email = "hubbard@berkeley.edu", role = "ctb"),
person("Mark", "van der Laan", email = "laan@stat.berkeley.edu",
role = "aut")
)
Author: Nima Hejazi [aut, cre, cph], Rachael Phillips [ctb], Alan Hubbard [ctb],
Mark van der Laan [aut]
Maintainer: Nima Hejazi <nhejazi@berkeley.edu>
Description: This package provides facilities for differential methylation
analysis based on variable importance measures (VIMs), a class of statistical
target parameters that arise in causal inference. The estimation and inference
procedures provided are nonparametric, relying on ensemble machine learning to
flexibly assess functional relationship among covariates and the outcome of
interest. These tools can be applied to differential methylation at the level
of CpG sites, with valid inference after multiple hypothesis testing.
Depends:
R (>= 3.4.0)
License: file LICENSE
URL: https://github.com/nhejazi/methyvim
BugReports: https://github.com/nhejazi/methyvim/issues
Encoding: UTF-8
LazyData: true
Imports:
stats,
methods,
ggplot2,
gridExtra,
superheat,
wesanderson,
magrittr,
dplyr,
gtools,
tmle,
future,
doFuture,
BiocParallel,
BiocGenerics,
SummarizedExperiment,
GenomeInfoDb,
bumphunter,
IRanges,
limma,
minfi,
methyvimData
Suggests:
testthat,
knitr,
rmarkdown,
BiocStyle,
SuperLearner,
randomForest,
earth,
gam
Remotes:
github::nhejazi/methyvimData
VignetteBuilder: knitr
RoxygenNote: 6.0.1
biocViews:
DNAMethylation,
DifferentialMethylation,
MethylationArray,
MethylSeq
nhejazi
mtmorgan
Update the following URL to point to the GitHub repository of the package you wish to submit to Bioconductor
Confirm the following by editing each check box to '[x]'
[x] I understand that by submitting my package to Bioconductor, the package source and all review commentary are visible to the general public.
[x] I have read the Bioconductor Package Submission instructions. My package is consistent with the Bioconductor Package Guidelines.
[x] I understand that a minimum requirement for package acceptance is to pass R CMD check and R CMD BiocCheck with no ERROR or WARNINGS. Passing these checks does not result in automatic acceptance. The package will then undergo a formal review and recommendations for acceptance regarding other Bioconductor standards will be addressed.
[x] My package addresses statistical or bioinformatic issues related to the analysis and comprehension of high throughput genomic data.
[x] I am committed to the long-term maintenance of my package. This includes monitoring the support site for issues that users may have, subscribing to the bioc-devel mailing list to stay aware of developments in the Bioconductor community, responding promptly to requests for updates from the Core team in response to changes in R or underlying software.
I am familiar with the essential aspects of Bioconductor software management, including:
For help with submitting your package, please subscribe and post questions to the bioc-devel mailing list.