Package: gimme
Title: Group Iterative Multiple Model Estimation
Version: 0.7-15
Date: 2023-08-29
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>
Depends: R (>= 3.5.0)
Imports: lavaan (>= 0.6-9), igraph (>= 1.0-0), qgraph, data.tree,
        MIIVsem(>= 0.5.4), imputeTS(>= 3.0), nloptr, graphics, stats,
        MASS, aTSA
Description: 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>. 
License: GPL-2
LazyData: true
URL: https://github.com/GatesLab/gimme/,
        https://tarheels.live/gimme/tutorials/
BugReports: https://github.com/GatesLab/gimme/issues
ByteCompile: true
RoxygenNote: 7.2.3
NeedsCompilation: no
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
Encoding: UTF-8
Packaged: 2023-08-29 17:29:10 UTC; kgates
Repository: CRAN
Date/Publication: 2023-08-29 17:50:02 UTC
Built: R 4.3.1; ; 2023-11-06 03:46:30 UTC; unix
