gimme0.7-18 package

Group Iterative Multiple Model Estimation

aggSEM

Group-level structural equation model search.

batch.create.tree

Create tree structures for group search solutions.

count.excellent

Counts number of excellent fit indices

create.tree

Create structure of group search solutions.

determine.subgroups

Determines subgroups.

expand.grid.unique

Provides unique combinations of two vectors.

final.org

Wrapup, create output files.

fit.model

Attempt to fit lavaan model.

get.params

Grabs final coefficients for each individual.

gimme-package

Group iterative multiple model estimation

gimmems.write

Write MS-GIMME results to data.frame.

gimmeSEM

Group iterative multiple model estimation.

highest.mi

Identifies highest MI from list of MIs.

indiv.search.ms

Individual-level search. Used in gimmeSEM, aggSEM, indSEM.

indiv.search

Individual-level search. Used in gimmeSEM, aggSEM, indSEM.

indSEM

Individual-level structural equation model search.

lowest.z

Identifies lowest z value from list of z values.

predict.gimme

GIMME Predicted Values.

prune.paths

Prunes paths. Ties together lowest.z and return.zs functions.

recode.vars

Recode variable names.

residuals.gimme

GIMME Residuals.

return.mis

Returns MIs from lavaan fit object.

return.zs

Returns z values from lavaan fit object.

search.paths.ms

Searches for paths. Ties together highest.mi and return.mis functions.

search.paths

Searches for paths. Ties together highest.mi and return.mis functions.

setupBaseSyntax

Set up base syntax file.

setupConvolve

Group iterative multiple model estimation.

setupDataLists

Create a list of dataframes

setupMultVarNames

Get names for bilinear effects.

setupPrelimDataChecks

Do some preliminary checks on the data.

setupPrepPaths

Allows user to open and close certain paths.

setupTransformData

Transform raw data as required.

sFIR

Estimate response function for each person using smoothed Finite Impul...

simulateVAR

Simulate data from Vector AutoRegression (VAR) models.

solution.tree

Solution trees for multiple solutions gimme.

subgroupStage

Create structure of group search solutions.

summaryPathsCounts

Create summary matrix of path counts and subgroup plots

w2e

Create edge list from weight matrix.

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>.

  • Maintainer: Kathleen M Gates
  • License: GPL-2
  • Last published: 2024-06-21