CMA
Synthesis of microarray-based classification
Bioconductor version: Release (2.9)
This package provides a comprehensive collection of various microarray-based classification algorithms both from Machine Learning and Statistics. Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed combined or stepwise in a user-friendly environment.
Author: Martin Slawski, Anne-Laure Boulesteix , Christoph Bernau .
Maintainer: Christoph Bernau
To install this package, start R and enter:
source("http:///biocLite.R")
biocLite("CMA")
To cite this package in a publication, start R and enter:
citation("CMA")
Documentation
| R Script | CMA_vignette.pdf | |
| Reference Manual |
Details
| biocViews | Classification |
| Depends | R, methods, stats, Biobase |
| Imports | |
| Suggests | MASS, class, nnet, glmnet, e1071, randomForest, plsgenomics, gbm, mgcv, corpcor, limma, st |
| System Requirements | |
| License | GPL (>= 2) |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Version | 1.12.0 |
| Since | Bioconductor 2.3 (R-2.8) |
Package Downloads
| Package Source | CMA_1.12.0.tar.gz |
| Windows Binary | CMA_1.12.0.zip (32- & 64-bit) |
| MacOS 10.5 (Leopard) binary | CMA_1.12.0.tgz |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Annotation
- Flow Cytometry and other assays
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