abr1 | abr1 dataset |
accest | Classification Wrapper Using Customised Classifiers |
dat.sel | Generate Pairwise Data Set Based on Class Labels |
dat.sel1 | Generate Data Set List |
feat.rank.re | Wrapper for Resampling Based Feature Ranking |
fiems_lct_main | LCT Mass Binning |
fiems_ltq_main | LTQ Mass Binning |
fs.mrpval | Significance of Feature Ranking |
fs.summary | Feature Ranking Resampling Summary |
fs.techniques | Implementation of Feature Ranking Techniques |
ftrank.agg | Aggregation of resampling based feature ranking results |
grpplot | Scatter Plot by Group |
hca.nlda | Hierarchical Clustering for Class 'nlda' |
koptimp | Imputation of Low Values |
mc.agg | Aggregation of classification results |
mc.comp.1 | Multiple Classifier Predictions Comparison |
mc.meas.iter | Summary of a predictor in mc.agg object |
mc.roc | Generate ROC curves from several classifiers |
mc.summary | Summary of multiple classifiers objects |
mean.shift | Mean Shift |
multibc | Multiple Metabolomics Fingerprint Baseline Correction |
nlda | Linear Discriminant Analysis for High Dimensional Problems |
onebc | Metabolomics Fingerprint Baseline Correction |
outl.det | Detection and Display Outliers |
panel.elli | panel.elli |
parse_freq | Output Variable Frequencies in Nested Lists |
parse_vec | Aggregation of Vectors in Nested Lists |
pca.comp | pca.comp |
plot.accest | Plot Method for Class 'accest' |
plot.mc.roc | Plot multiple ROC curves |
plot.nlda | Plot Method for Class 'nlda' |
predict.nlda | Classify Multivariate Observations by 'nlda' |
preproc | Data Tranformation Wrapper |
summ.ftrank | Summarise multiple resampling based feature ranking outputs |
ticstats | Compute and Display Total Ion Count (TIC) statistics |
tidy.ftrank | Tidy up multiple resampling based ranking results. |
trainind | Generation of Training Samples Indices |
trainind.cv | Constrained Generation of Training Samples Indices |
valipars | Generate Control Parameters For Validation / Resampling |
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