Nothing
gimme: Group Iterative Multiple Model Estimation
Data-driven approach for arriving at person-specific time series models. 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. See Gates & Molenaar (2012) <doi:10.1016/j.neuroimage.2012.06.026>.
Browse package contents
Package details |
|
|---|---|
| Author | Stephanie Lane [aut, trl], Kathleen Gates [aut, cre, ccp], Zachary Fisher [aut], Cara Arizmendi [aut], Peter Molenaar [aut, ccp], Edgar Merkle [ctb], Michael Hallquist [ctb], Hallie Pike [ctb], Teague Henry [ctb], Kelly Duffy [ctb], Lan Luo [ctb], Adriene Beltz [csp], Aidan Wright [csp], Jonathan Park [ctb], Sebastian Castro Alvarez [ctb] |
| Maintainer | Kathleen M Gates <gateskm@email.unc.edu> |
| License | GPL-2 |
| Version | 0.7-15 |
| URL | https://github.com/GatesLab/gimme/ https://tarheels.live/gimme/tutorials/ |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the gimme package in your browser
Any scripts or data that you put into this service are public.
