Agilent annotation data (chip rgug4105a) assembled using data from public repositories.
Superclasses PostgreSQL connection to help enable full dplyr functionality on Redshift'.
Redcarpet is an extensible Ruby library for Markdown processing and conversion to (X)HTML.
Agilent Rat annotation data (chip rgug4130a) assembled using data from public repositories.
This package is an automatically generated RnBeads annotation package for the assembly hg19.
This gem extends ruby-rdf with several common RDF vocabularies.
Agilent "Rat Genome, Whole" annotation data (chip rgug4131a) assembled using data from public repositories.
Genome wide annotation for Rat, primarily based on mapping using Entrez Gene identifiers.
MCMC based sampling of binary matrices with fixed margins as used in exact Rasch model tests.
Generates graphs, CSV files, and coordinates related to river valleys when calling the riverbuilder() function.
Convert REDCap exports into tidy tables for easy handling of REDCap repeat instruments and event arms.
Carrying out inferences about any linear combination of proportions and the ratio of two proportions.
This package provides tools for filtering occurrence records, generating alpha-hull-derived range polygons and mapping species distributions.
This package provides the headers and static library of Protocol buffers for other R packages to compile and link against.
This package provides color schemes for maps (and other graphics) designed by Cynthia Brewer as described at http://colorbrewer2.org
This package provides fast machine learning algorithms including matrix factorization and divisive clustering for large sparse and dense matrices.
This package provides mappings from Entrez gene identifiers to various annotations for the genome of the rat.
This package provides a collection of palettes designed to integrate with ggplot', reflecting the color schemes associated with ConesaLab'.
Rust programming language toolchain
Measure YARD documentation coverage
Integers of various widths.
This package provides the Breiman and Cutler's random forests algorithm, based on a forest of trees using random inputs, for classification and regression.
Package of data sets from "Mathematical Statistics with Resampling in R" (1st Ed. 2011, 2nd Ed. 2018) by Laura Chihara and Tim Hesterberg.