atoms                   A tibble containing the NIST standard atomic
                        weights
calc_km                 Calculate the Kendrick mass
calc_kmd                Calculate the Kendrick mass defect (KMD)
calc_neutral_loss       Calculate neutral losses from precursor ion
                        mass and fragment ion masses
calc_nominal_km         Calculate the nominal Kendrick mass
collapse_max            Collapse intensities of technical replicates by
                        calculating their maximum
collapse_mean           Collapse intensities of technical replicates by
                        calculating their mean
collapse_median         Collapse intensities of technical replicates by
                        calculating their median
collapse_min            Collapse intensities of technical replicates by
                        calculating their minimum
create_metadata_skeleton
                        Create a blank metadata skeleton
filter_blank            Filter Features based on their occurrence in
                        blank samples
filter_cv               Filter Features based on their coefficient of
                        variation
filter_global_mv        Filter Features based on the absolute number or
                        fraction of samples it was found in
filter_grouped_mv       Group-based feature filtering
filter_msn              Filter Features based on occurrence of fragment
                        ions
filter_mz               Filter Features based on their mass-to-charge
                        ratios
filter_neutral_loss     Filter Features based on occurrence of neutral
                        losses
formula_to_mass         Calculate the monoisotopic mass from a given
                        formula
impute_bpca             Impute missing values using Bayesian PCA
impute_global_lowest    Impute missing values by replacing them with
                        the lowest observed intensity (global)
impute_knn              Impute missing values using nearest neighbor
                        averaging
impute_lls              Impute missing values using Local Least Squares
                        (LLS)
impute_lod              Impute missing values by replacing them with
                        the Feature 'Limit of Detection'
impute_mean             Impute missing values by replacing them with
                        the Feature mean
impute_median           Impute missing values by replacing them with
                        the Feature median
impute_min              Impute missing values by replacing them with
                        the Feature minimum
impute_nipals           Impute missing values using NIPALS PCA
impute_ppca             Impute missing values using Probabilistic PCA
impute_rf               Impute missing values using random forest
impute_svd              Impute missing values using Singular Value
                        Decomposition (SVD)
impute_user_value       Impute missing values by replacing them with a
                        user-provided value
join_metadata           Join a featuretable and sample metadata
msn_calc_nl             Calculate neutral losses from precursor ion
                        mass and fragment ion masses
msn_scale               Scale intensities in MSn spectra to the highest
                        value within each spectrum
normalize_cyclic_loess
                        Normalize intensities across samples using
                        cyclic LOESS normalization
normalize_factor        Normalize intensities across samples using a
                        normalization factor
normalize_median        Normalize intensities across samples by
                        dividing by the sample median
normalize_pqn           Normalize intensities across samples using a
                        Probabilistic Quotient Normalization (PQN)
normalize_quantile_all
                        Normalize intensities across samples using
                        standard Quantile Normalization
normalize_quantile_batch
                        Normalize intensities across samples using
                        grouped Quantile Normalization with multiple
                        batches
normalize_quantile_group
                        Normalize intensities across samples using
                        grouped Quantile Normalization
normalize_quantile_smooth
                        Normalize intensities across samples using
                        smooth Quantile Normalization (qsmooth)
normalize_ref           Normalize intensities across samples using a
                        reference feature
normalize_sum           Normalize intensities across samples by
                        dividing by the sample sum
plot_pca                Draws a scores or loadings plot or performs
                        calculations necessary to draw them manually
plot_volcano            Draws a Volcano Plot or performs calculations
                        necessary to draw one manually
read_featuretable       Read a feature table into a tidy tibble
read_mgf                Read a MGF file into a tidy tibble
scale_auto              Scale intensities of features using autoscale
scale_center            Center intensities of features around zero
scale_level             Scale intensities of features using level
                        scaling
scale_pareto            Scale intensities of features using Pareto
                        scaling
scale_range             Scale intensities of features using range
                        scaling
scale_vast              Scale intensities of features using vast
                        scaling
scale_vast_grouped      Scale intensities of features using grouped
                        vast scaling
summary_featuretable    General information about a feature table and
                        sample-wise summary
toy_metaboscape         A small toy data set created from a feature
                        table in MetaboScape style
toy_metaboscape_metadata
                        Sample metadata for the fictional dataset
                        'toy_metaboscape'
toy_mgf                 A small toy data set containing MSn spectra
transform_log           Transforms the intensities by calculating their
                        log
transform_power         Transforms the intensities by calculating their
                        _n_th root
