uscbiostats / binarydosage

Converts VCF files to a binary format

USCbiostats

Name	Description
AnnoQR	R client wrap for AnnoQ API (https://github.com/blueOwl/AnnoQR)
aphylo	Statistical inference of genetic functions in phylogenetic trees
bayesnetworks	C++ program to fit Bayesian networks, illustrated with simulated data
BinaryDosage	Converts VCF files to a binary format
causnet	What the Package Does (One Line, Title Case)
fdrci	Permutation-Based FDR Point and Confidence Interval Estimation
fmcmc	A friendly MCMC framework
GxEScanR	R version of GxEScan
HiLDA	An R package for inferring the mutational exposures difference between groups
hJAM	hJAM is a hierarchical model which unifies the framework of Mendelian Randomization and Transcriptome-wide association studies.
iMutSig	A web application to identify the most similar mutational signature using shiny
jsPhyloSVG	htmlwidgets for the jsPhyloSVG JavaScript library
LUCIDus	Latent and Unknown Cluster Analysis using Integrated Data
MergeBinaryDosage	R package for merging binary dosage files
partition	A fast and flexible framework for agglomerative partitioning in R
pfamscanr	An R client for EMBL-EBI’s PfamScan API
polygons	Flexible functions for computing polygons coordinates in R
rphyloxml	Read and write phyloXML files in R
selectKSigs	Selection of K in finding the number of mutational signatures
slurmR	slurmR: A Lightweight Wrapper for Slurm
xrnet	R Package for Hierarchical Regularized Regression to Incorporate External Data
xtune	An R package for Lasso and Ridge Regression with differential penalization based on prior knowledge

binarydosage's Issues

New Core C/D Assessment Project Template

This document provides a general framework for assessing the software and computing needs of a particular project.

The first step consists on answering the following questions:

• In a paragraph, describe what is the project about.
• List the project's specific goals (e.g. make the method available for public use, create a webservice, present this analysis)
• Think about what are your computing/software needs, and list them.

Once a general overview of the project has been defined, the next step consists on analyzing the following:

R packages development

Fundamental questions/tasks

• Check for other R packages/software that does what the project tries to do.
• Describe the ultimate goal of your R package (e.g. solve an optim problem, implement a visualization problem, estimate parameters, etc.)
• Think about data (if any needed), in particular, input/output, have you checked any R packages that already have this?
• Create a repository for the package.

Once the project is on git, following checkmarks

• The package uses roxygen2 (you can turn your package Rd files to roxygen using Rd2roxygen).
• The package's functions are throughly documented. This includes: Description, parameters (@param), value (@return), details (if any), examples (@details), references (@references) and links to other functions (@seealso, and @family).
• The package has been fully tested using testthat or other framework.
• The project is built using Continuous Integration services as Travis-ci and Appveyor (use the usethis package to set it up, eg: usethis::use_travis).
• The package includes a vignette with an extended example and, if needed, references on the package.
• The package has a website (suggest using pkgdown)
• The package has been uploaded to CRAN or BioC.

Reading large VCF files

Hello!
Thanks for this pretty useful tool! We were interested using your package to compute the R2 value after merging multiple imputed datasets. However, it takes a really long time to read the merged vcf file, since it contains more than 30,000 samples. Would there be a way to parallelize the reading using the vcftobd command? Otherwise, what would you recommend for my needs?

Thanks a lot for your help?