Package: gimme 10.0

Kathleen M Gates

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>.

Authors:Stephanie Lane [aut, trl], Kathleen M 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], Björn Siepe [ctb]

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DESCRIPTION
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gimme/json (API)

# Install 'gimme' in R:

install.packages('gimme', repos = c('https://gateslab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/gateslab/gimme/issues

Datasets:

HRFsim - Hemodynamic Response Function (HRF) GIMME example.
ms.fit - Fitted gimme object with multiple solutions
simData - Large example, heterogeneous data, group, subgroup, and individual level effects.
simDataLV - Latent variable example, heterogeneous data, group, subgroup level effects.
ts - Small example, heterogeneous data, group and individual level effects

On CRAN:

8.10 score 31 stars 84 scripts 861 downloads 1 mentions 7 exports 132 dependencies

Last updated from:194e1d1bb3. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	210
source / vignettes	OK	218
linux-release-x86_64	OK	257
macos-release-arm64	OK	155
macos-oldrel-arm64	OK	191
windows-devel	OK	144
windows-release	OK	144
windows-oldrel	OK	134
wasm-release	OK	166

Exports:aggSEM convolveFIR gimme gimmeSEM indSEM simulateVAR solution.tree

Dependencies:abind backports base64enc boot broom bslib cachem car carData checkmate cli cluster colorspace commonmark corpcor cowplot cpp11 curl data.table data.tree Deriv digest doBy dplyr evaluate farver fastmap fdrtool fontawesome forecast foreign Formula fracdiff fs generics ggplot2 ggtext glasso glue GPArotation gridExtra gridtext gtable gtools highr Hmisc htmlTable htmltools htmlwidgets igraph imputeTS isoband jpeg jquerylib jsonlite knitr labeling lattice lavaan lifecycle litedown lme4 lmtest magrittr markdown MASS Matrix MatrixModels memoise mgcv microbenchmark MIIVsem mime minqa mnormt modelr nlme nloptr nnet numDeriv pbapply pbivnorm pbkrtest pillar pkgconfig plyr png psych purrr qgraph quadprog quantmod quantreg R6 rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack reformulas reshape2 rlang rmarkdown rpart rstudioapi S7 sass scales SparseM stinepack stringi stringr survival tibble tidyr tidyselect timeDate tinytex tseries TTR urca utf8 vctrs viridisLite withr xfun xml2 xts yaml zoo

Group Iterative Multiple Model Estimation (GIMME)

The Basics | Running GIMME | Output | FAQ

Rendered fromgimme_vignette.Rmdusingknitr::rmarkdown

Last update: 2026-06-11
Started: 2024-06-18

Help page	Topics
Group iterative multiple model estimation	gimme-package
Group-level structural equation model search.	aggSEM
Group iterative multiple model estimation.	gimme gimmeSEM
Hemodynamic Response Function (HRF) GIMME example.	HRFsim
Individual-level structural equation model search.	indSEM
Fitted gimme object with multiple solutions	ms.fit
Group iterative multiple model estimation.	convolve convolveFIR setupConvolve
Large example, heterogeneous data, group, subgroup, and individual level effects.	simData
Latent variable example, heterogeneous data, group, subgroup level effects.	simDataLV
Simulate data from Vector AutoRegression (VAR) models.	simulateVAR
Solution trees for multiple solutions gimme.	solution.tree
Small example, heterogeneous data, group and individual level effects	ts

Package: gimme 10.0

gimme: Group Iterative Multiple Model Estimation

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