common-arguments: List of common arguments
In ForeCA: Forecastable Component Analysis

Common arguments used in several functions in this package.

`series`	a T \times K array with `T` observations from the K-dimensional time series \mathbf{X}_t. Can be a `matrix`, `data.frame`, or a multivariate `ts` object.
`U`	a T \times K array with `T` observations from the K-dimensional whitened (`whiten`) time series \mathbf{U}_t. Can be a `matrix`, `data.frame`, or a multivariate `ts` object.
`mvspectrum.output`	an object of class `"mvspectrum"` representing the multivariate spectrum of \mathbf{X}_t (not necessarily `normalize`d).
`f.U`	multivariate spectrum of class `'mvspectrum'` with `normalize = TRUE`.
`algorithm.control`	list; control settings for any iterative ForeCA algorithm. See `complete_algorithm_control` for details.
`entropy.control`	list; control settings for entropy estimation. See `complete_entropy_control` for details.
`spectrum.control`	list; control settings for spectrum estimation. See `complete_spectrum_control` for details.
`entropy.method`	string; method to estimate the entropy from discrete probabilities p_i; here probabilities are the spectral density evaluated at the Fourier frequencies, \widehat{p}_i = \widehat{f}(ω_i).
`spectrum.method`	string; method for spectrum estimation; see `method` argument in `mvspectrum`.
`threshold`	numeric; values of spectral density below `threshold` are set to 0; default `threshold = 0`.
`smoothing`	logical; if `TRUE` the spectrum will be smoothed with a nonparametric estimate using `gam` and an exponential family (with `link = log`). Only works for univariate spectrum. The smoothing parameter is chosen automatically using generalized cross-validation (see `gam` for details). Default: `FALSE`.
`base`	logarithm base; entropy is measured in “nats” for `base = exp(1)`; in “bits” if `base = 2` (default).