文件列表:

marbox\ar1.m (475, 1998-07-10)
marbox\ar2.m (774, 1998-07-10)
marbox\ar_spec.m (636, 1998-06-29)
marbox\ar_spectra.m (536, 1998-07-07)
marbox\coherent.m (352, 1998-07-10)
marbox\Contents.m (1718, 1998-07-10)
marbox\demmar1.m (480, 1998-07-10)
marbox\demmar2.m (989, 1998-07-10)
marbox\freq_ar2.m (161, 1998-06-09)
marbox\incoherent.m (837, 1998-07-07)
marbox\mar_cohere.m (298, 1998-07-07)
marbox\mar_cohere_compare.m (504, 1998-07-07)
marbox\mar_cov.m (864, 1998-06-26)
marbox\mar_cov_allspec.m (1008, 1998-07-10)
marbox\mar_cov_cohere.m (1825, 1998-07-07)
marbox\mar_cov_spec.m (1049, 1998-07-07)
marbox\mar_gen.m (868, 1998-07-07)
marbox\mar_learn.m (1105, 1998-07-07)
marbox\mar_plot_spectra.m (442, 1998-07-10)
marbox\mar_spec.m (329, 1998-07-10)
marbox\mar_spectra.m (557, 1998-07-07)
marbox\plot_cohere.m (1227, 1998-06-18)
marbox\rmar.m (1069, 1998-06-17)
marbox (0, 2017-08-26)

MARBOX, version 1.1, William Penny, July 19*** A matlab toolbox for multivariate autoregressive (MAR) models The core routines for using MAR models are: % mar_learn get MAR model from multiple time series % mar_gen generate mutiple time series from MAR model % mar_spectra calculate Hermitian PSD matrix from MAR model % mar_cohere get coherence between channels from MAR model % mar_spec get individual spectral densities from MAR model % rmar running MAR models in overlapping windows The other routines are more or less historical accidents. They routines use a common data structure 'mar' with fields mar.p order of model mar.lag(k).a coeff matrix at lag k mar.noise_cov noise covariance matrix The mar_learn routine is basically a wrapper for the ARX function in the system identification toolbox, so you'll need that on your matlab search path.

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