The currently available baseline correction algorithms in pybaselines can broadly be categorized as polynomial, whittaker, morphological, smooth, spline, classification, optimizers, and miscellaneous (misc) methods. Note that this is simply for grouping code and helping to explain the internals of this library and NOT meant as a hard-classification of the field of baseline correction (Please stop blindly copying this section in papers. There are numerous types of baseline correction algorithms that are not included within pybaselines, which is why baseline correction in general is such an absolutely fascinating field! Besides, miscellaneous is obviously not an actual type of baseline correction...)

This section of the documentation is to help provide some context for each algorithm. In addition, most algorithms will have a figure that shows how well the algorithm fits various datasets to help choose the correct algorithm for a particular baseline. These datasets include noisy data, data with both positive and negative peaks, data with overlapping peaks, and concave data, and they serve as a way to quickly filter out algorithms that would not work for a particular dataset.

Refer to the API section of the documentation for the full parameter and reference listing for any algorithm.