Package index • metabom8

All functions

add_note(): Add user note to metabom8 provenance Appends a user annotation to the "m8_prep" attribute. The step title is formatted as "note {username}". The timestamp is stored in params, and the user message is stored in notes.
align_segment(): Align NMR Spectra in a Selected Shift Region
align_spectra(): Cohort-Guided Interval Alignment for 1D NMR Spectra
balanced_boot(): Balanced bootstrap resampling strategy
balanced_mc(): Balanced Monte-Carlo resampling strategy
binning(): Spectral data binning
calibrate(): Chemical Shift Calibration
cliffs_d() es_cdelta(): Cliff's Delta Effect Size
correct_baseline() bline(): Baseline Correction for Spectral Data
correct_lw(): Linewidth correction by scaling spectra to a reference linewidth
covid: COVID-19 blood plasma proton NMR spectra (processed)
covid_raw: COVID-19 blood plasma proton NMR spectra (raw)
cv_anova(): Cross-validated ANOVA for O-PLS models
dmodx(): Distance to the Model in X-Space (DModX)
.perm_test_from_table(): Permutation-test summary from an opls_perm out_df table
ellipse2d(): Calculate 2D Hotelling T^2 Ellipse
excise(): Excise Chemical Shift Regions from 1D NMR Spectra
fitted(): Extract fitted Y values
get_idx() get.idx(): Select Indices for a Chemical Shift Region
get_provenance(): Retrieve metabom8 provenance metadata
hiit_raw: High-intensity interval training (HIIT) 1H NMR urine dataset
hotellingsT2(): Hotelling T^2 Statistic
kfold(): K-fold cross-validation strategy
list_preprocessing(): List available preprocessing steps
loadings(<m8_model>): Model loadings
lw(): Full Width at Half Maximum (FWHM) Estimation
summary(<m8_model>) show(<m8_model>): m8_model class Model object returned by pca(), pls(), and opls().
mc(): Monte-Carlo cross-validation strategy
metabom8-package metabom8: metabom8: A High-Performance R Package for Metabolomics Modeling and Analysis
minmax(): Min-Max Scaling to \([0,1]\)
noise_sd(): Estimate Noise Standard Deviation in 1D NMR Spectra
norm_eretic(): Normalise Spectra Using ERETIC Signal
opls(): Fit an Orthogonal Partial Least Squares (O-PLS) model
opls_perm(): OPLS Model Validation via Y-Permutation
pareto_scaling(): Pareto Scaling Leaves variables unscaled. Optional centering.
pca(): Principal Component Analysis (PCA)
plotStocsy(): Plot STOCSY result
plot_spec() spec() matspec(): Plot 1D NMR Spectra
pls(): Fit a Partial Least Squares (PLS) model
ppick(): Find Local Extrema in NMR Spectra (Peak Picking)
ppick2(): Peak picking using Savitzky–Golay derivatives
pqn(): Probabilistic Quotient Normalisation (PQN)
prep_X(): Applies a preprocessing strategy to a numeric matrix.
print_preprocessing(): List available preprocessing functions Returns the preprocessing utilities provided by metabom8.
print_provenance(): Print metabom8 preprocessing pipeline
read1d() read1d_proc(): Import 1D NMR spectra (TopSpin processed)
read1d_raw(): Read raw FIDs and process to spectra
scRange(): Min-Max Scaling to Arbitrary Range
scores(): PLS/OPLS model scores
stocsy(): Statistical Total Correlation Spectroscopy (STOCSY)
storm(): Subset Optimisation by Reference Matching (STORM)
stratified_kfold(): Y-stratified k-fold cross-validation strategy
unscaled(): No Scaling This function defines a preprocessing strategy that is applied via prep_X.
uv_scaling(): Unit Variance Scaling This function defines a preprocessing strategy that is applied via prep_X.
vip(): Variable Importance in Projection (VIP)
weights(): Extract model weights
xres(): Compute X residual matrix Returns the residual matrix (E) of an OPLS model.