This package fits Rossell's generalizations of the Gamma-Gamma hierarchical model for microarray data analysis, which substantially improve the quality of the fit at a low computational cost. The model can be fit via empirical Bayes (Expectation-Maximization and Simulated Annealing) and fully Bayesian techniques (Gibbs and Metropolis-Hastings posterior sampling). Routines are provided to perform differential expression analysis and class prediction.
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("gaga")
| Manual for the gaga library | R Script | |
|---|---|---|
| Reference Manual |
| biocViews | |
|---|---|
| Depends |
R
,
Biobase
,
coda
|
| Imports | |
| Suggests | |
| System Requirements | |
| License | GPL (>=2) |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Development History | Bioconductor Changelog |
| Package source | gaga_1.4.0.tar.gz |
|---|---|
| Windows binary | gaga_1.4.0.zip |
| MacOS X 10.4 (Tiger) binary | gaga_1.4.0.tgz |
| MacOS X 10.5 (Leopard) binary | gaga_1.4.0.tgz |
| Package Downloads Report | Downloads Stats |