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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("GeneticsPed")
In most cases, you don't need to download the package archive at all.
GeneticsPed
Pedigree and genetic relationship functions
Bioconductor version: 2.14
Classes and methods for handling pedigree data. It also includes functions to calculate genetic relationship measures as relationship and inbreeding coefficients and other utilities. Note that package is not yet stable. Use it with care!
Author: Gregor Gorjanc and David A. Henderson <dnadave at comcast.net>, with code contributions by Brian Kinghorn and Andrew Percy (see file COPYING)
Maintainer: David Henderson <DNADavenator at GMail.Com>
Citation (from within R,
enter citation("GeneticsPed")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("GeneticsPed")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("GeneticsPed")
| R Script | Calculation of genetic relatedness/relationship between individuals in the pedigree | |
| R Script | Pedigree handling | |
| R Script | Quantitative genetic (animal) model example in R | |
| Reference Manual | ||
| Text | NEWS | |
| Text | LICENSE |
Details
| biocViews | Genetics, Software |
| Version | 1.26.0 |
| In Bioconductor since | BioC 2.0 (R-2.5) |
| License | LGPL (>= 2.1) |
| Depends | R (>= 2.4.0), gdata (>= 2.3.0), genetics (>= 1.3.0), MASS |
| Imports | |
| Suggests | RUnit, gtools |
| System Requirements | |
| URL | http://rgenetics.org |
| Depends On Me | |
| Imports Me | |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | GeneticsPed_1.26.0.tar.gz |
| Windows Binary | GeneticsPed_1.26.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | GeneticsPed_1.26.0.tgz |
| Mac OS X 10.9 (Mavericks) | |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
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