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Sparse PCA with L1-penalized loadings for variable selection
Smooths each row of the input data (or computes its derivatives) using a Savitzky-Golay filter.
Sparse PARAFAC fitting for incomplete multi-way data via Levenberg-Marquardt
Automated baseline correction, peak detection and grouping for chromatographic data
Fast image super-resolution using conjugate gradient optimization for microscopy and imaging applications.
Legacy weighted SIMPLISMA (pure) for selecting pure variables in multivariate datasets.
Shifted Covariates REgression Analysis for Multi-way data
The Fourier filter filters data in the frequency domain to remove unwanted noise and artifacts.
Simulates GC-MS datasets with retention time shifts for PARAFAC2 benchmarking
SIT decomposes multi-way data from GC-MS by modeling shift-invariant tri-linear structure, 20-60x faster than PARAFAC2.