B C D E F G J K L M N O P Q R S T V W
CMA-package | Synthesis of microarray-based classification |
best | Show best hyperparameter settings |
best-method | Show best hyperparameter settings |
bklr | Internal functions |
bklr.predict | Internal functions |
bkreg | Internal functions |
boxplot-method | Make a boxplot of the classifier evaluation |
care.dev | Internal functions |
care.exp | Internal functions |
characterplot | Internal functions |
classification | General method for classification with various methods |
classification-method | General method for classification with various methods |
classification-methods | General method for classification with various methods |
cloutput | "cloutput" |
cloutput-class | "cloutput" |
clvarseloutput | "clvarseloutput" |
clvarseloutput-class | "clvarseloutput" |
CMA | Synthesis of microarray-based classification |
compare | Compare different classifiers |
compare-method | Compare different classifiers |
compare-methods | Compare different classifiers |
compBoostCMA | Componentwise Boosting |
compBoostCMA-method | Componentwise Boosting |
compBoostCMA-methods | Componentwise Boosting |
dldaCMA | Diagonal Discriminant Analysis |
dldaCMA-method | Diagonal Discriminant Analysis |
dldaCMA-methods | Diagonal Discriminant Analysis |
ElasticNetCMA | Classfication and variable selection by the ElasticNet |
ElasticNetCMA-method | Classfication and variable selection by the ElasticNet |
ElasticNetCMA-methods | Classfication and variable selection by the ElasticNet |
evaloutput | "evaloutput" |
evaloutput-class | "evaloutput" |
evaluation | Evaluation of classifiers |
evaluation-method | Evaluation of classifiers |
evaluation-methods | Evaluation of classifiers |
fdaCMA | Fisher's Linear Discriminant Analysis |
fdaCMA-method | Fisher's Linear Discriminant Analysis |
fdaCMA-methods | Fisher's Linear Discriminant Analysis |
flexdaCMA | Flexible Discriminant Analysis |
flexdaCMA-method | Flexible Discriminant Analysis |
flexdaCMA-methods | Flexible Discriminant Analysis |
ftable-method | Cross-tabulation of predicted and true class labels |
ftest | Filter functions for Gene Selection |
gbmCMA | Tree-based Gradient Boosting |
gbmCMA-method | Tree-based Gradient Boosting |
gbmCMA-methods | Tree-based Gradient Boosting |
GenerateLearningsets | Repeated Divisions into learn- and tets sets |
genesel | "genesel" |
genesel-class | "genesel" |
GeneSelection | General method for variable selection with various methods |
GeneSelection-method | General method for variable selection with various methods |
GeneSelection-methods | General method for variable selection with various methods |
golub | ALL/AML dataset of Golub et al. (1999) |
golubcrit | Filter functions for Gene Selection |
join | Combine list elements returned by the method classification |
join-method | Combine list elements returned by the method classification |
join-methods | Combine list elements returned by the method classification |
khan | Small blue round cell tumor dataset of Khan et al. (2001) |
knnCMA | Nearest Neighbours |
knnCMA-method | Nearest Neighbours |
knnCMA-methods | Nearest Neighbours |
kruskaltest | Filter functions for Gene Selection |
LassoCMA | L1 penalized logistic regression |
LassoCMA-method | L1 penalized logistic regression |
LassoCMA-methods | L1 penalized logistic regression |
ldaCMA | Linear Discriminant Analysis |
ldaCMA-method | Linear Discriminant Analysis |
ldaCMA-methods | Linear Discriminant Analysis |
learningsets | "learningsets" |
learningsets-class | "learningsets" |
limmatest | Filter functions for Gene Selection |
mklr | Internal functions |
mklr.predict | Internal functions |
mkreg | Internal functions |
my.care.exp | Internal functions |
nnetCMA | Feed-forward Neural Networks |
nnetCMA-method | Feed-Forward Neural Networks |
nnetCMA-methods | Feed-Forward Neural Networks |
obsinfo | Classifiability of observations |
obsinfo-method | "evaloutput" |
pknnCMA | Probabilistic Nearest Neighbours |
pknnCMA-method | Probabilistic nearest neighbours |
pknnCMA-methods | Probabilistic nearest neighbours |
Planarplot | Visualize Separability of different classes |
Planarplot-method | Visualize Separability of different classes |
Planarplot-methods | Visualize Separability of different classes |
plot-method | Probability plot |
plot-method | Barplot of variable importance |
plot-method | Visualize results of tuning |
plotprob | Internal functions |
plrCMA | L2 penalized logistic regression |
plrCMA-method | L2 penalized logistic regression |
plrCMA-methods | L2 penalized logistic regression |
pls_ldaCMA | Partial Least Squares combined with Linear Discriminant Analysis |
pls_ldaCMA-method | Partial Least Squares combined with Linear Discriminant Analysis |
pls_ldaCMA-methods | Partial Least Squares combined with Linear Discriminant Analysis |
pls_lrCMA | Partial Least Squares followed by logistic regression |
pls_lrCMA-method | Partial Least Squares followed by logistic regression |
pls_lrCMA-methods | Partial Least Squares followed by logistic regression |
pls_rfCMA | Partial Least Squares followed by random forests |
pls_rfCMA-method | Partial Least Squares followed by random forests |
pls_rfCMA-methods | Partial Least Squares followed by random forests |
pnnCMA | Probabilistic Neural Networks |
pnnCMA-method | Probabilistic Neural Networks |
pnnCMA-methods | Probabilistic Neural Networks |
prediction | General method for predicting classes of new observations |
prediction-method | General method for predicting class lables of new observations |
prediction-methods | General method for predicting class lables of new observations |
predoutput | "predoutput" |
predoutput-class | "predoutput" |
qdaCMA | Quadratic Discriminant Analysis |
qdaCMA-method | Quadratic Discriminant Analysis |
qdaCMA-methods | Quadratic Discriminant Analysis |
rfCMA | Classification based on Random Forests |
rfCMA-method | Classification based on Random Forests |
rfCMA-methods | Classification based on Random Forests |
rfe | Filter functions for Gene Selection |
roc | Receiver Operator Characteristic |
roc-method | Receiver Operator Characteristic |
ROCinternal | Internal functions |
roundvector | Internal functions |
rowswaps | Internal functions |
safeexp | Internal functions |
scdaCMA | Shrunken Centroids Discriminant Analysis |
scdaCMA-method | Shrunken Centroids Discriminant Analysis |
scdaCMA-methods | Shrunken Centroids Discriminant Analysis |
show-method | "cloutput" |
show-method | "evaloutput" |
show-method | "genesel" |
show-method | "learningsets" |
show-method | "predoutput" |
show-method | "tuningresult" |
show-method | "wmcr.result" |
shrinkcat | Filter functions for Gene Selection |
shrinkldaCMA | Shrinkage linear discriminant analysis |
shrinkldaCMA-method | Shrinkage linear discriminant analysis |
shrinkldaCMA-methods | Shrinkage linear discriminant analysis |
summary-method | Summarize classifier evaluation |
svmCMA | Support Vector Machine |
svmCMA-method | Support Vector Machine |
svmCMA-methods | Support Vector Machine |
toplist | Display 'top' variables |
toplist-method | Display 'top' variables |
ttest | Filter functions for Gene Selection |
tune | Hyperparameter tuning for classifiers |
tune-method | Hyperparameter tuning for classifiers |
tune-methods | Hyperparameter tuning for classifiers |
tuningresult | "tuningresult" |
tuningresult-class | "tuningresult" |
varseloutput | "varseloutput" |
varseloutput-class | "varseloutput" |
weighted.mcr | Tuning / Selection bias correction |
weighted.mcr-method | General method for tuning / selection bias correction |
weighted.mcr-methods | General method for tuning / selection bias correction |
welchtest | Filter functions for Gene Selection |
wilcoxtest | Filter functions for Gene Selection |
wmc | Tuning / Selection bias correction based on matrix of subsampling fold errors |
wmc-method | General method for tuning / selection bias correction based on a matrix of subsampling fold errors. |
wmc-methods | General method for tuning / selection bias correction based on a matrix of subsampling fold errors. |
wmcr.result | "wmcr.result" |
wmcr.result-class | "wmcr.result" |