![[R logo]](../../../doc/html/logo.jpg){kind=logo}
![[Up]](../../../doc/html/left.jpg){kind=small}
![[Top]](../../../doc/html/up.jpg){kind=small}
A B C D F G H I K M N O P R S T U V X
| aov.all.vars | Analysis of variance |
| aov.one.var | Analysis of variance of one variable |
| apply.filter.function | Apply filter function |
| background.correction | Background correction |
| baseline.correction | Baseline correction |
| boxplot.variables | Boxplot of variables |
| calculate.ellipses | Calculate ellipses |
| calculate.shifts | Calculate shifts |
| check.dataset | Check dataset |
| clustering | Perform cluster analysis |
| convert.to.factor | Convert metadata to factor |
| correlations.dataset | Dataset correlations |
| count.missing.values | Count missing values |
| count.missing.values.per.sample | Count missing values per sample |
| count.missing.values.per.variable | Count missing values per variable |
| create.dataset | Create dataset |
| data.correction | Data correction |
| dataset.from.peaks | Dataset from peaks |
| dendrogram.plot | Plot dendrogram |
| dendrogram.plot.col | Plot dendrogram |
| feature.selection | Perform feature selection |
| filter.feature.selection | Perform selection by filter |
| find.equal.samples | Find equal samples |
| first.derivative | First derivative |
| flat.pattern.filter | Flat pattern filter |
| flat.pattern.filter.percentage | Flat pattern filter by percentage |
| flat.pattern.filter.threshold | Flat pattern filter by threshold |
| fold.change | Fold change analysis |
| get.all.intensities | Get all intensities. |
| get.data | Get data |
| get.data.as.df | Get data as data frame |
| get.data.value | Get data value |
| get.data.values | Get data values |
| get.intensity | Get intensity |
| get.metadata | Get metadata |
| get.metadata.value | Get metadata value |
| get.metadata.var | Get metadata variable |
| get.overall.freq.list | Get overall frequencies list |
| get.peak.values | Get peak values |
| get.sample.names | Get sample names |
| get.type | Get type of data |
| get.value.label | Get value label |
| get.x.label | Get x-axis label |
| get.x.values.as.num | Get x-axis values as numbers |
| get.x.values.as.text | Get x-axis values as text |
| heatmap.correlations | Correlations heatmap |
| hierarchical.clustering | Perform hierarchical clustering analysis |
| impute.nas.knn | Impute missing values with KNN |
| impute.nas.linapprox | Impute missing values with linear approximation |
| impute.nas.mean | Impute missing values with mean |
| impute.nas.median | Impute missing values with median |
| impute.nas.value | Impute missing values with value replacement |
| is.spectra | Check type of data |
| kmeans.clustering | Perform k-means clustering analysis |
| kmeans.plot | Plot kmeans clusters |
| kmeans.result.df | Show cluster's members |
| merge.datasets | Merge two datasets |
| metadata.as.variables | Metadata as variables |
| missingvalues.imputation | Missing values imputation |
| msc.correction | Multiplicative scatter correction |
| multiClassSummary | Multi Class Summary |
| multifactor.aov.all.vars | Multifactor ANOVA |
| multifactor.aov.onevar | One Variable Multifactor ANOVA |
| normalize | Normalize data |
| num.samples | Get number of samples |
| num.x.values | Get number of x values |
| offset.correction | Offset correction |
| pca.analysis.dataset | PCA analysis (classical) |
| pca.biplot | PCA biplot |
| pca.biplot3D | 3D PCA biplot (interactive) |
| pca.importance | PCA importance |
| pca.kmeans.plot2D | 2D PCA k-means plot |
| pca.kmeans.plot3D | 3D PCA k-means plot (interactive) |
| pca.pairs.kmeans.plot | PCA k-means pairs plot |
| pca.pairs.plot | PCA pairs plot |
| pca.plot.3d | 3D pca plot |
| pca.robust | PCA analysis (robust) |
| pca.scoresplot2D | 2D PCA scores plot |
| pca.scoresplot3D | 3D PCA scores plot |
| pca.scoresplot3D.rgl | 3D PCA scores plot (interactive) |
| pca.screeplot | PCA scree plot |
| peaks.per.sample | Peaks per sample |
| peaks.per.samples | Peaks per samples |
| plot.spectra | Plot spectra |
| plot.spectra.simple | Plot spectra (simple) |
| predict.samples | Predict samples |
| read.csvs.folder | Read CSVs from folder |
| read.data.csv | Read CSV data |
| read.dataset.csv | Read dataset from CSV |
| read.metadata | Read metadata |
| read.multiple.csvs | Read multiple CSVs |
| recursive.feature.elimination | Perform recursive feature elimination |
| remove.peaks.interval | Remove interval of peaks |
| remove.peaks.interval.sample.list | Remove interval of peaks (sample list) |
| replace.data.value | Replace data value |
| replace.metadata.value | Replace metadata's value |
| savitzky.golay | Savitzky-golay transformation |
| set.metadata | Set new metadata |
| set.sample.names | Set samples names |
| set.value.label | Set value label |
| set.x.label | Set x-label |
| set.x.values | Set new x-values |
| shift.correction | Shift correction |
| smoothing.interpolation | Smoothing interpolation |
| smoothing.spcbin.hyperspec | Wavelength binning |
| smoothing.spcloess.hyperspec | Loess smoothing |
| sum.dataset | Dataset summary |
| summary.var.importance | Summary of variables importance |
| train.and.predict | Train and predict |
| train.classifier | Train classifier |
| train.models.performance | Train models |
| tTests.dataset | t-Tests on dataset |
| tTests.pvalue | t-Tests on matrix |
| univariate.analysis | Univariate Analysis |
| values.per.peak | Values per peak |
| var.importance | Variables importance |
| variables.as.metadata | Variables as metadata |
| x.values.to.indexes | Get x-values indexes |
| xvalue.interval.to.indexes | Get indexes of an interval of x-values |