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Data-driven approach for arriving at person-specific time series models from within a Graphical Vector Autoregression (VAR) framework. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. All estimates are obtained uniquely for each individual in the final models. The method for the 'graphicalVAR' approach is found in Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | graphicalVAR, here, qgraph, png |
| Suggests: | knitr, rmarkdown |
| Published: | 2024-05-16 |
| Author: | Sandra Williams Lee [aut, cre], Kathleen M. Gates [aut] |
| Maintainer: | Sandra Williams Lee <wsandra at live.unc.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | GIMMEgVAR results |
| Reference manual: | GIMMEgVAR.pdf |
| Vignettes: |
GIMMEgVAR Vignette |
| Package source: | GIMMEgVAR_0.1.0.tar.gz |
| Windows binaries: | r-devel: GIMMEgVAR_0.1.0.zip, r-release: GIMMEgVAR_0.1.0.zip, r-oldrel: GIMMEgVAR_0.1.0.zip |
| macOS binaries: | r-release (arm64): GIMMEgVAR_0.1.0.tgz, r-oldrel (arm64): GIMMEgVAR_0.1.0.tgz, r-release (x86_64): GIMMEgVAR_0.1.0.tgz, r-oldrel (x86_64): GIMMEgVAR_0.1.0.tgz |
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