OptionalalgorithmOptionalcontrolControl points to determine which weights to update.
OptionalfactorFactor used to calculate the threshold for determining outliers in the residuals. Higher values mean more tolerance for outliers. The default value is based on noise follow the normal distribution values over 3 times the standard-deviation could be marked as signals or outliers.
OptionallambdaFactor of weights matrix in -> [I + lambda D'D]z = x
OptionallearningLearning rate for weight updates.
OptionalmaxMaximum number of iterations for the baseline refinement process.
OptionaltoleranceTolerance for convergence. The process stops if the change in baseline is less than this value.
OptionalweightsArray of weights
Solver algorithm to use for the smoothing routine.
'cholesky'— build the full (symmetric) system and solve using a Cholesky decomposition. Generally more robust for small/medium arrays and when a matrix factorization is acceptable.'thomas'— use a tridiagonal formulation and solve with the Thomas algorithm (specialised tridiagonal solver). Lower memory usage and faster for large inputs when the system is tridiagonal.