This package provides tools for representing and modeling data in the EMBL-EBI GWAS catalog.

This package provides functions to plot data associated with arbitrary genomic intervals along chromosomal ideogram.

This package provides basic plotting, data manipulation and processing of mass spectrometry based proteomics data.

Read and write feather files, a lightweight binary columnar data store designed for maximum speed.

Generate a colorized diff of two R objects for an intuitive visualization of their differences.

This package lets you expand factors, characters and other eligible classes into dummy/indicator variables.

This package provides lots of plotting, various labeling, axis and color scaling functions for R.

This package provides an implementation of scale functions for setting axis breaks of a ggplot.

This package provides a collection of functions to implement a class for univariate polynomial manipulations.

This package provides a helper that tests DBI back ends for conformity to the interface.

This package provides a renameat2 command that calls the Linux-specific renameat2 system call.

Slop provides a Ruby domain specific language for gathering options and parsing command line flags.

Bump provides commands to manage Rubygem versioning, updating to the next patch version for example.

Slop provides a Ruby domain specific language for gathering options and parsing command line flags.

Nenv provides a convenient wrapper for Ruby's ENV to modify and inspect the environment.

This package provides a collection of text algorithms: Levenshtein, Soundex, Metaphone, Double Metaphone, Porter Stemming.

This package provides a Ruby module that provides a two-phase lock with a counter.

Causal network analysis methods for regulator prediction and network reconstruction from genome scale data.

Produce highly customizable publication quality graphics for genomic data primarily at the cohort level.

representation of public Iyer data from http://genome-www.stanford.edu/serum/clusters.html.

This package provides support for parallelized estimation of GLMs/GEEs, catering for dispersed data.

Given the scores from decision makers, the analytic hierarchy process can be conducted easily.

Enables filtering datasets by a prior specified identifiers which correspond to saved filter expressions.

Writes SAS code to get predicted values from every tree of a gbm.object.