Function Reference

The table below is a partial reference for SIFT functions. Not all functions are documented in this list.

	Function Name	Description
GUI functions	pop_pre_prepData	generate GUI for data preprocessing
pop_est_fitMVAR	generate GUI for VAR/AMVAR model fitting
pop_est_selModelOrder	generate GUI for VAR model order selection
pop_est_validateMVAR	generate GUI for VAR model validation
pop_est_mvarConnectivity	generate GUI for computing connectivity measures
pop_vis_TimeFreqGrid	generate GUI for Interactive Time-Frequency Grid
pop_vis_causalBrainMovie3D	generate GUI for Interactive Causal BrainMovie3D
Preprocessing	pre_detrend	linearly detrend or center an ensemble of data
pre_diffData	apply a difference filter to an ensemble of data
pre_normData	apply temporal or ensemble normalization to an ensemble of data
pre_prepData	preprocess an ensemble of data (calls other subfunctions)
pre_selectComps	select a set of independent components from the data
Modeling	est_calcInvCovMat	compute inverse covariance matrix of a VAR process
est_calcInvCovMatFourier	compute frequency-domain transformation of the inverse covariance matrix of a VAR process
est_calcInvCovMatFourierPDC	same as above, but a specific version used for analytic PDC significance thresholds
est_checkMVARConsistency	check the percent consistency of a fitted VAR model
est_checkMVARParams	perform a sanity check on a set of specified MVAR parameters – return recommendations on optimal parameters, if relevant.
est_checkMVARStability	check the stability/stationarity of fitted VAR model
est_checkMVARWhiteness	check the whiteness of the residuals of a fitted VAR model
est_eigenmode	return the eigenmodes of a VAR process (requires ARFIT package)
est_fitMVAR	fit a VAR[p] model to the data using one of several algorithms (Vieira-Morf, ARFIT, MLS, etc). Optionally can use a sliding window to perform segmentation-based adaptive MVAR analysis. Calls modified routines from Alois Schloegl’s open-source TSA package or from the ARFIT package.
est_fitMVARKalman	fit a VAR[p] model to continuous data using a Kalman filter. Adapts code from Alois Schloegl’s open-source TSA package
est_MVARConnectivity	compute spectral density, coherence, and connectivity measures from a fitted VAR model
est_mvarResiduals	return the residuals of a fitted VAR model
est_mvtransfer	compute frequency-domain quantities from a VAR model (spectrum, coherence, granger-causality, etc)
est_selModelOrder	evaluate and return model order selection criteria (AIC, SBC, FPE, HQ) for a range of model orders
Statistics	stat_bootSignificanceTests	perform bootstrap significance tests on connectivity structure
stat_analyticSignificanceTests	perform asymptotic analysis significance tests on connectivity structure
stat_phaserand	return a distribution satisfying the null hypothesis of no connectivity using a phase-randomization approach (Theiler, 1997)
stat_bootstrap	return a bootstrapped distribution of a spectral/connectivity estimator
stat_prctileTest	perform one- or two-sided percentile tests to compare an observed value with the quantiles of a (null) distribution.
Visualization	vis_TimeFreqGrid	low-level function to create an interactive Time-Frequency Grid
vis_TimeFreqCell	low-level function to render an expanded (detailed) version of a single cell of the Time-Frequency Grid
vis_causalBrainMovie3D	low-level function to generate a causal BrainMovie3D
vis_causalProjection	in development – low-level function to generate a Causal Projection image or movie
Simulations	sim_genVARModelFromEq	generate an arsim()-compatible VAR specification from a text-based equation
Helpers	hlp_*	A large number of helper functions (to be documented later)