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SIT decomposes multi-way data from GC-MS by modeling shift-invariant tri-linear structure, 20-60x faster than PARAFAC2.
Shifted Covariates REgression Analysis for Multi-way data
Smooths each row of the input data (or computes its derivatives) using a Savitzky-Golay filter.
OALS calculates the scores and loadings from data with missing values.
Bootstrap BCa confidence limits for PCA scores and loadings
Normalized 2D radial pair distributions for molecular hydration analysis
Multiblock PCA and PLS methods for multi-source data analysis
Kernelize transforms bilinear data (exponential decays) using kernels to provide trilinear data.
Jchemo is a Julia package for regression, classification and preprocessing developed for chemometrics but useful across various data analysis tasks.
Interval Correlation Optimized Shifting (iCOshift) for fast, robust signal alignment across samples.