Package: gimme 0.9.3

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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manual.pdf |manual.html✨
card.svg |card.png
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:

Conda:

8.10 score 31 stars 72 scripts 1.1k downloads 1 mentions 7 exports 132 dependencies

Last updated from:66ebf592cd. Checks:7 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING203
source / vignettesOK235
linux-release-x86_64WARNING192
macos-release-arm64WARNING151
macos-oldrel-arm64WARNING218
windows-develWARNING156
windows-releaseWARNING175
windows-oldrelWARNING149
wasm-releaseOK145

Exports:aggSEMconvolveFIRgimmegimmeSEMindSEMsimulateVARsolution.tree

Dependencies:abindbackportsbase64encbootbroombslibcachemcarcarDatacheckmatecliclustercolorspacecommonmarkcorpcorcowplotcpp11curldata.tabledata.treeDerivdigestdoBydplyrevaluatefarverfastmapfdrtoolfontawesomeforecastforeignFormulafracdifffsgenericsggplot2ggtextglassoglueGPArotationgridExtragridtextgtablegtoolshighrHmischtmlTablehtmltoolshtmlwidgetsigraphimputeTSisobandjpegjquerylibjsonliteknitrlabelinglatticelavaanlifecyclelitedownlme4lmtestmagrittrmarkdownMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkMIIVsemmimeminqamnormtmodelrnlmenloptrnnetnumDerivpbapplypbivnormpbkrtestpillarpkgconfigplyrpngpsychpurrrqgraphquadprogquantmodquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasreshape2rlangrmarkdownrpartrstudioapiS7sassscalesSparseMstinepackstringistringrsurvivaltibbletidyrtidyselecttimeDatetinytextseriesTTRurcautf8vctrsviridisLitewithrxfunxml2xtsyamlzoo

Group Iterative Multiple Model Estimation (GIMME)

KM Gates

Rendered fromgimme_vignette.Rmdusingknitr::rmarkdownon May 24 2026.

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