| apLCMS-package | Adaptive processing of LC/MS data |
| adaptive.bin | Adaptive binning |
| adaptive.bin.2 | Adaptive binning specifically for the machine learning approach. |
| adduct.table | A table of potential adducts. |
| adjust.time | Adjust retention time across spectra. |
| apLCMS | Adaptive processing of LC/MS data |
| cdf.to.ftr | Convert a number of cdf files in the same directory to a feature table |
| cont.index | Continuity index |
| eic.disect | Internal function: Extract data feature from EIC. |
| EIC.plot | Plot extracted ion chromatograms |
| EIC.plot.learn | Plot extracted ion chromatograms based on the machine learning method output |
| eic.pred | Internal function: calculate the score for each EIC based on prediction of match status. |
| eic.qual | Internal function: Calculate the single predictor quality. |
| feature.align | Align peaks from spectra into a feature table. |
| features | Sample feature tables from 4 profiles |
| find.match | Internal function: finding the best match between a set of detected features and a set of known features. |
| find.tol | An internal function that is not supposed to be directly accessed by the user. Find m/z tolerance level. |
| find.tol.time | An internal function that is not supposed to be directly accessed by the user. Find elution time tolerance level. |
| find.turn.point | Find peaks and valleys of a curve. |
| interpol.area | Interpolate missing intensities and calculate the area for a single EIC. |
| learn.cdf | Peak detection using the machine learning approach. |
| load.lcms | Loading LC/MS data. |
| make.known.table | Producing a table of known features based on a table of metabolites and a table of allowable adducts. |
| mass.match | An internal function: finding matches between two vectors of m/z values. |
| merge_seq_3 | An internal function. |
| metabolite.table | A known metabolite table based on HMDB. |
| peak.characterize | Internal function: Updates the information of a feature for the known feature table. |
| plot_cdf_2d | Plot the data in the m/z and retention time plane. |
| plot_txt_2d | Plot the data in the m/z and retention time plane. |
| present.cdf.3d | Generates 3 dimensional plots for LCMS data. |
| proc.cdf | Filter noise and detect peaks from LC/MS data in CDF format |
| proc.cdf.2d | Compute a 2D Binned Kernel Density Estimate from LC/MS data in CDF format. |
| proc.txt | Filter noise and detect peaks from LC/MS data in text format |
| prof | Sample profile data after noise filtration by the run filter |
| prof.to.features | Generate feature table from noise-removed LC/MS profile |
| recover.weaker | Recover weak signals in some profiles that is not identified as a peak, but corresponds to identified peaks in other spectra. |
| rm.ridge | Removing long ridges at the same m/z. |
| semi.sup | Semi-supervised feature detection |
| semi.sup.2d | Semi-supervised feature detection using 2D peak detection |
| semi.sup.learn | Semi-supervised feature detection using machine learning approach. |
| target.search | Targeted search of metabolites with given m/z and (optional) retention time |
| two.step.hybrid | Two step hybrid feature detection. |
| two.step.hybrid.2d | Two step hybrid feature detection using 2D peak detection. |