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) |