gma: Granger Mediation Analysis
Performs Granger mediation analysis (GMA) for time series. This package includes a single level GMA model and a two-level GMA model, for time series with hierarchically nested structure. The single level GMA model for the time series of a single participant performs the causal mediation analysis which integrates the structural equation modeling and the Granger causality frameworks. A vector autoregressive model of order p is employed to account for the spatiotemporal dependencies in the data. Meanwhile, the model introduces the unmeasured confounding effect through a nonzero correlation parameter. Under the two-level model, by leveraging the variabilities across participants, the parameters are identifiable and consistently estimated based on a full conditional likelihood or a two-stage method. See Zhao, Y., & Luo, X. (2017), Granger Mediation Analysis of Multiple Time Series with an Application to fMRI, <arXiv:1709.05328> for details.
| Version: |
1.0 |
| Depends: |
MASS, nlme, car |
| Published: |
2017-09-19 |
| Author: |
Yi Zhao, Xi Luo |
| Maintainer: |
Yi Zhao <zhaoyi1026 at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| In views: |
CausalInference |
| CRAN checks: |
gma results |
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