CMA-package {CMA} | R Documentation |
The aim of the package is to provide a user-friendly
environment for the evaluation of classification methods using
gene expression data. A strong focus is on combined variable selection,
hyperparameter tuning, evaluation, visualization and comparison of (up to now) 21
classification methods from three main fields: Discriminant Analysis,
Neural Networks and Machine Learning. Although the package has been
created with the intention to be used for Microarray data, it can as well
be used in various (p > n)
-scenarios.
Package: | CMA |
Type: | Package |
Version: | 0.8.5 |
Date: | 2008-2-13 |
License: | GPL (version 2 or later) |
Most Important Steps for the workflow are:
GenerateLearningsets
GeneSelection
tune
classification
using 1.-3.compBoostCMA
, dldaCMA
, ElasticNetCMA
,
fdaCMA
, flexdaCMA
, gbmCMA
,
knnCMA
, ldaCMA
, LassoCMA
,
nnetCMA
, pknnCMA
, plrCMA
,
pls_ldaCMA
, pls_lrCMA
, pls_rfCMA
,
pnnCMA
, qdaCMA
, rfCMA
,
scdaCMA
, shrinkldaCMA
, svmCMA
evaluation
and make a comparison
by calling compare
Martin Slawski martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
Maintainer: Martin Slawski martin.slawski@campus.lmu.de.