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Normalizes an input matrix using various norms and dimensions for flexible data scaling.
Plots correlation map with values to make interpretation easier.
Removes Rayleigh and Raman scatter from fluorescence EEM data
Sparse PARAFAC fitting for incomplete multi-way data via Levenberg-Marquardt
Merging and fusing multiple calibration models optimally
Performs binning on an N-dimensional array.
Autoscale standardizes data by centering it to zero mean and scaling it to unit variance.
Kernelize transforms bilinear data (exponential decays) using kernels to provide trilinear data.
Simulates GC-MS datasets with retention time shifts for PARAFAC2 benchmarking
Fast image super-resolution using conjugate gradient optimization for microscopy and imaging applications.