abr1 |
abr1 dataset |
accest |
Classification Wrapper Using Customised Classifiers |
accest.default |
Classification Wrapper Using Customised Classifiers |
accest.dlist |
Classification Wrapper Using Customised Classifiers |
accest.formula |
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 |
feat.rank.re.default |
Wrapper for Resampling Based Feature Ranking |
feat.rank.re.dlist |
Wrapper for Resampling Based Feature Ranking |
feat.rank.re.formula |
Wrapper for Resampling Based Feature Ranking |
FIEmspro |
abr1 dataset |
fiems_lct_main |
LCT Mass Binning |
fiems_ltq_main |
LTQ Mass Binning |
fs.anova |
Implementation of Feature Ranking Techniques |
fs.auc |
Implementation of Feature Ranking Techniques |
fs.bw |
Implementation of Feature Ranking Techniques |
fs.kruskal |
Implementation of Feature Ranking Techniques |
fs.mi |
Implementation of Feature Ranking Techniques |
fs.mrpval |
Significance of Feature Ranking |
fs.relief |
Implementation of Feature Ranking Techniques |
fs.rf |
Implementation of Feature Ranking Techniques |
fs.snr |
Implementation of Feature Ranking Techniques |
fs.summary |
Feature Ranking Resampling Summary |
fs.techniques |
Implementation of Feature Ranking Techniques |
fs.welch |
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.agg.default |
Aggregation of classification results |
mc.comp.1 |
Multiple Classifier Predictions Comparison |
mc.comp.1.default |
Multiple Classifier Predictions Comparison |
mc.meas.iter |
Summary of a predictor in mc.agg object |
mc.roc |
Generate ROC curves from several classifiers |
mc.roc.default |
Generate ROC curves from several classifiers |
mc.summary |
Summary of multiple classifiers objects |
mc.summary.default |
Summary of multiple classifiers objects |
multibc |
Multiple Metabolomics Fingerprint Baseline Correction |
nlda |
Linear Discriminant Analysis for High Dimensional Problems |
nlda.default |
Linear Discriminant Analysis for High Dimensional Problems |
nlda.formula |
Linear Discriminant Analysis for High Dimensional Problems |
onebc |
Metabolomics Fingerprint Baseline Correction |
outl.det |
Detection and Display Outliers |
parse_freq |
Output Variable Frequencies in Nested Lists |
parse_vec |
Aggregation of Vectors in Nested Lists |
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 |
print.accest |
Classification Wrapper Using Customised Classifiers |
print.feat.rank.re |
Wrapper for Resampling Based Feature Ranking |
print.mc.agg |
Aggregation of classification results |
print.mc.comp.1 |
Multiple Classifier Predictions Comparison |
print.mc.summary |
Summary of multiple classifiers objects |
print.nlda |
Linear Discriminant Analysis for High Dimensional Problems |
print.summary.accest |
Classification Wrapper Using Customised Classifiers |
print.summary.nlda |
Linear Discriminant Analysis for High Dimensional Problems |
summ.ftrank |
Summarise multiple resampling based feature ranking outputs |
summary.accest |
Classification Wrapper Using Customised Classifiers |
summary.nlda |
Linear Discriminant Analysis for High Dimensional Problems |
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 |