The path to the directory where the data files are located, or the name of the list containing each individual's time series. Each file or matrix must contain one matrix for each individual containing a T (time) by p (number of variables) matrix where the columns represent variables and the rows represent time. If in list form, each item in the list (i.e., matrix) must be named.

The path to the directory where the results will be stored (optional). If specified, a copy of output files will be replaced in directory. If directory at specified path does not exist, it will be created.

The spacing of the data files when data are in a directory. "" indicates space-delimited, "/t" indicates tab-delimited, "," indicates comma delimited. Only necessary to specify if reading data in from physical directory.

Logical. Indicate TRUE for data files with a header, FALSE otherwise. Only necessary to specify if reading data in from physical directory.

Logical. If TRUE, begins search for group model with autoregressive (AR) paths open. Defaults to TRUE.

Logical. If TRUE, figures depicting relations among variables of interest will automatically be created. For aggregate-level plot, red paths represent positive weights and blue paths represent negative weights. Dashed lines denote lagged relations (lag 1) and solid lines are contemporaneous (lag 0). Defaults to TRUE.

lavaan-style syntax containing paths with which to begin model estimation (optional). That is, Y~X indicates that Y is regressed on X, or X predicts Y. Paths can also be set to a specific value for estimation using lavaan-style syntax (e.g., 'V4 ~ 0.5*V3'), or set to 0 so that they will not be estimated (e.g., 'V4 ~ 0*V3'). If no header is used, then variables should be referred to with V followed (with no separation) by the column number. If a header is used, variables should be referred to using variable names. To reference lag variables, "lag" should be added to the end of the variable name with no separation. Defaults to NULL.

Vector of variable names to be treated as exogenous (optional). That is, exogenous variable X can predict Y but cannot be predicted by Y. If no header is used, then variables should be referred to with V followed (with no separation) by the column number. If a header is used, variables should be referred to using variable names. Defaults to NULL.

Vector of variable names to be treated as outcome (optional). This is a variable that can be predicted by others but cannot predict. If no header is used, then variables should be referred to with V followed (with no separation) by the column number. If a header is used, variables should be referred to using variable names.

Vector of variable names to be convolved via smoothed Finite Impulse Response (sFIR). Defaults to NULL.

Expected response length in seconds. For functional MRI BOLD, 16 seconds (default) is typical for the hemodynamic response function.

Interval between data acquisition. Currently conv_length/conv_interval must be a constant. For fMRI studies, this is the repetition time. Defaults to 1.

Vector of variable names to be multiplied to explore bilinear/modulatory effects (optional). All multiplied variables will be treated as exogenous (X can predict Y but cannot be predicted by Y). Within the vector, multiplication of two variables should be indicated with an asterik (e.g. V1*V2). If no header is used, variables should be referred to with V followed by the column number (with no separation). If a header is used, each variable should be referred to using variable names. If multiplication with the lag 1 of a variable is desired, the variable name should be followed by "lag" with no separation (e.g. V1*V2lag). Note that if multiplied variables are desired, at least one variable in the dataset must be specified as exogenous. Defaults to NULL.

Logical. If TRUE, the variables indicated in mult_vars will be mean-centered before being multiplied together. Defaults to FALSE.

Logical. If TRUE, all variables will be standardized to have a mean of zero and a standard deviation of one. Defaults to FALSE.

Logical. If TRUE, enables hybrid-VAR models where both directed contemporaneous paths and contemporaneous covariances among residuals are candidate relations in the search space. Defaults to FALSE.

Logical. If true, VAR models where contemporaneous covariances among residuals are candidate relations in the search space. Defaults to FALSE.

A vector of fit indices from lavaan.

A list of all datasets.

A character vector containing syntax that never changes.

The number of subjects in the sample.

Cutoff used in order for MI to be considered significant.

A data frame containing the order of the files and the names of the files. Used to merge in subgroup assignment and preserve order.

A character vector containing eligible paths that gimme is allowed to add to the model. Ensures only EPCs from allowable paths are considered in the creation of the similarity matrix.

A dataframe with the first column a string vector of data file names without extensions and the second vector a integer vector of subgroup labels.

A character vector containing variable names.

A character vector containing variable names.

Logical. TRUE means that combinations are kept where a variable appears twice.

A list containing information created in setup().

A list containing group-level information. NULL in aggSEM and indSEM.

A list containing subgroup information.

A list containing information specific to each subgroup.

A list containing output from indiv.search().

if subgroup = TRUE, eligable paths for potential individual-level search

A character vector containing syntax.

A data frame containing individual data set.

A list containing information created in setup().

A list containing group-level information. NULL in aggSEM and indSEM.

A list containing individual- and (potentially) subgroup-level information.

The counter indicating the individual.

The path to the directory where the data files are located, or the name of the list containing each individual's time series. Each file or matrix must contain one matrix for each individual containing a T (time) by p (number of variables) matrix where the columns represent variables and the rows represent time. Individuals must have the same variables (p) but can have different lengths of observations (T).

The path to the directory where the results will be stored (optional). If specified, a copy of output files will be replaced in directory. If directory at specified path does not exist, it will be created.

The spacing of the data files. Follows R convention. "" indicates space-delimited, backslash "t" indicates tab-delimited, "," indicates comma delimited. Only necessary to specify if reading data in from physical directory.

Logical. Indicate TRUE for data files with a header. Only necessary to specify if reading data in from physical directory.

Logical. If TRUE, begins search for group model with autoregressive (AR) paths freed for estimation. If ms_allow=TRUE, it is recommended to set ar=FALSE. Multiple solutions are unlikely to be found when ar=TRUE. Defaults to TRUE.

Logical. If TRUE, graphs depicting relations among variables of interest will automatically be created. Solid lines represent contemporaneous relations (lag 0) and dashed lines reflect lagged relations (lag 1). For individual-level plots, red paths represent positive weights and blue paths represent negative weights. Width of paths corresponds to estimated path weight. For the group-level plot, black represents group-level paths, grey represents individual-level paths, and (if subgroup = TRUE) green represents subgroup-level paths. For the group-level plot, the width of the edge corresponds to the count. Defaults to TRUE.

Logical. If TRUE, subgroups are generated based on similarities in model features using the walktrap.community function from the igraph package. When ms_allow=TRUE, subgroup should be set to FALSE. Defaults to FALSE.

Option to indicate feature(s) used to subgroup individuals. Defaults to "lag & contemp" for lagged and contemporaneous, which is the original method. Can use "lagged" or "contemp" to subgroup solely on features related to lagged and contemporaneous relations, respectively.

Community detection method used to cluster individuals into subgroups. Options align with those available in the igraph package: "Walktrap" (default), "Infomap", "Louvain", "Edge Betweenness", "Label Prop", "Fast Greedy", "Leading Eigen", and "Spinglass".

Threshold for inducing sparsity in similarity matrix. Options are: the percent of edges in the similarity matrix to set to zero (e.g., .25 would set the lower quartile), "lowest" (default) subtracts the minimum value from all values, and "search" searches across thresholds to arrive at one providing highest modularity.

Dataframe. Option only available when subgroup = TRUE. Dataframe should contain two columns. The first column should specify file labels (the name of the data files without file extension), and the second should contain integer values (beginning at 1) specifying the subgroup membership for each individual. function from the igraph package. Defaults to TRUE.

lavaan-style syntax containing paths with which to begin model estimation (optional). That is, Y~X indicates that Y is regressed on X, or X predicts Y. Paths can also be set to a specific value for estimation using lavaan-style syntax (e.g., 'V4 ~ 0.5*V3'), or set to 0 so that they will not be estimated (e.g., 'V4 ~ 0*V3'). If no header is used, then variables should be referred to with V followed (with no separation) by the column number. If a header is used, variables should be referred to using variable names. To reference lag variables, "lag" should be added to the end of the variable name with no separation. Defaults to NULL.

Vector of variable names to be treated as exogenous (optional). That is, exogenous variable X can predict Y but cannot be predicted by Y. If no header is used, then variables should be referred to with V followed (with no separation) by the column number. If a header is used, variables should be referred to using variable names. The default for exogenous variables is that lagged effects of the exogenous variables are not included in the model search. If lagged paths are wanted, "&lag" should be added to the end of the variable name with no separation. Defaults to NULL.

Vector of variable names to be treated as outcome (optional). This is a variable that can be predicted by others but cannot predict. If no header is used, then variables should be referred to with V followed (with no separation) by the column number. If a header is used, variables should be referred to using variable names.

Vector of variable names to be convolved via smoothed Finite Impulse Response (sFIR). Note, conv_vars are not not automatically considered exogenous variables. To treat conv_vars as exogenous use the exogenous argument. Variables listed in conv_vars must be binary variables. You cannot do lagged variables. If there is missing data in the endogenous variables their values will be imputed for the convolution operation only. Defaults to NULL.

Expected response length in seconds. For functional MRI BOLD, 16 seconds (default) is typical for the hemodynamic response function.

Interval between data acquisition. Currently conv_length/conv_interval must be an integer. For fMRI studies, this is the repetition time. Defaults to 1.

Vector of variable names to be multiplied to explore bilinear/modulatory effects (optional). All multiplied variables will be treated as exogenous (X can predict Y but cannot be predicted by Y). Within the vector, multiplication of two variables should be indicated with an asterik (e.g. V1*V2). If no header is used, variables should be referred to with V followed by the column number (with no separation). If a header is used, each variable should be referred to using variable names. If multiplication with the lag 1 of a variable is desired, the variable name should be followed by "lag" with no separation (e.g. V1*V2lag).

Logical. If TRUE, the variables indicated in mult_vars will be mean-centered before being multiplied together. Defaults to FALSE.

Logical. If TRUE, all variables will be standardized to have a mean of zero and a standard deviation of one. Defaults to FALSE

Cutoff value for group-level paths. Defaults to .75, indicating that a path must be significant across 75% of individuals to be included as a group-level path.

Cutoff value for subgroup- level paths. Defaults to .75, indicating that a path must be significant across at least 75% of the individuals in a subgroup to be considered a subgroup-level path.

Logical. If TRUE provides internal output for diagnostic purposes. Defaults to FALSE.

Logical. If TRUE provides multiple solutions when more than one path has identical modification index values. When ms_allow=TRUE, it is recommended to set ar=FALSE. Multiple solutions are unlikely to be found when ar=TRUE. Additionally, subgroup should be set to FALSE. Output files for individuals with multiple solutions will represent the last solution found for the individual, not necessarily the best solution for the individual.

Precision used when evaluating similarity of modification indices when ms_allow = TRUE. We recommend that ms_tol not be greater than the default, especially when standardize=TRUE. Defaults to 1e-5.

Invoke latent variable modeling by providing the measurement model syntax here. lavaan conventions are used for relating observed variables to factors. Defaults to NULL.

Estimator used for factor analysis. Options are "miiv" (default), "pml" (pseudo-ML) or "svd".

Method used for estimating latent variable scores from parameters obtained from the factor analysis when lv_model is not NULL. Options are: "regression" (Default), "bartlett".

Type of scaling indicator to use when "miiv" selected for lv_estimator. Options are "first.indicator" (Default; the first observed variable in the measurement equation is used), "group" (best one for the group), or "individual" (each individual has the best one for them according to R2).

Estimator for final estimations. "miiv" (Default) or "pml" (pseudo-ML).

When not null, invokes multiLASSO approach for the GIMME model search procedure. Arguments indicate the model selection criterion to use for model selection: 'bic' (select on BIC), 'aic', 'aicc', 'hqc', 'cv' (cross-validation).

Logical. If TRUE, enables hybrid-VAR models where both directed contemporaneous paths and contemporaneous covariances among residuals are candidate relations in the search space. Defaults to FALSE.

Logical. If true, VAR models where contemporaneous covariances among residuals are candidate relations in the search space. Defaults to FALSE.

Option to require that the directionality of a relation has to be higher than the reverse direction for a prespecified proportion of indivdiuals.

A character vector containing the names of all ordered categorical variables in the model.

Indicate how to correct for multiple testing. "Bonferoni Group" (Default) corrects the alpha value for the number of people (N) in th sample; "Bonferoni Paths" corrects according to the number of eligible paths for that individual; a numeric <1 and >0 can be entered to indicate the alpha level desired.

A list of MIs across individuals

A list of fit indices. Only relevant at the individual-level.

A character vector containing eligible paths that gimme is allowed to add to a model (e.g., no nonsense paths).

The proportion of individuals for whom a path must be significant in order for it to be added to the models. NULL if used at the individual-level.

The number of subjects in a given stage of the search. If in the group stage, n_subj equals the number of subjects. If in the subgroup stage, n_subj equals the number of individuals in a given subgroup. At the individual stage, n_subj = 1.

Cutoff used in order for MI to be considered significant. Value varies depending on stage of search (e.g., group, subgroup, individual).

The path to the directory where the data files are located, or the name of the list containing each individual's time series. Each file or matrix must contain one matrix for each individual containing a T (time) by p (number of variables) matrix where the columns represent variables and the rows represent time.

The path to the directory where the results will be stored (optional). If specified, a copy of output files will be replaced in directory. If directory at specified path does not exist, it will be created.

The spacing of the data files. "" indicates space-delimited, "/t" indicates tab-delimited, "," indicates comma delimited. Only necessary to specify if reading data in from physical directory.

Logical. Indicate TRUE for data files with a header. Only necessary to specify if reading data in from physical directory.

Logical. If TRUE, begins search for individual models with autoregressive (AR) paths open. Defaults to TRUE.

Logical. If TRUE, graphs depicting relations among variables of interest will automatically be created. Defaults to TRUE. For individual- level plots, red paths represent positive weights and blue paths represent negative weights.

lavaan-style syntax containing paths with which to begin model estimation. That is, Y~X indicates that Y is regressed on X, or X predicts Y. Paths can also be set to a specific value for estimation using lavaan-style syntax (e.g., 'V4 ~ 0.5*V3'), or set to 0 so that they will not be estimated (e.g., 'V4 ~ 0*V3'). If no header is used, then variables should be referred to with V followed (with no separation) by the column number. If a header is used, variables should be referred to using variable names. To reference lag variables, "lag" should be added to the end of the variable name with no separation. Defaults to NULL.

Vector of variable names to be treated as exogenous. That is, exogenous variable X can predict Y but cannot be predicted by Y. If no header is used, then variables should be referred to with V followed (with no separation) by the column number. If a header is used, variables should be referred to using variable names. Defaults to NULL.

Vector of variable names to be treated as outcome (optional). This is a variable that can be predicted by others but cannot predict. If no header is used, then variables should be referred to with V followed (with no separation) by the column number. If a header is used, variables should be referred to using variable names.

Vector of variable names to be convolved via smoothed Finite Impulse Response (sFIR). Defaults to NULL.

Expected response length in seconds. For functional MRI BOLD, 16 seconds (default) is typical for the hemodynamic response function.

Interval between data acquisition. Currently conv_length/conv_interval must be a constant. For fMRI studies, this is the repetition time. Defaults to 1.

Vector of variable names to be multiplied to explore bilinear/modulatory effects (optional). All multiplied variables will be treated as exogenous (X can predict Y but cannot be predicted by Y). Within the vector, multiplication of two variables should be indicated with an asterik (e.g. V1*V2). If no header is used, variables should be referred to with V followed by the column number (with no separation). If a header is used, each variable should be referred to using variable names. If multiplication with the lag 1 of a variable is desired, the variable name should be followed by "lag" with no separation (e.g. V1*V2lag). Note that if multiplied variables are desired, at least one variable in the dataset must be specified as exogenous. Defaults to NULL.

Logical. If TRUE, the variables indicated in mult_vars will be mean-centered before being multiplied together. Defaults to FALSE.

Logical. If TRUE, all variables will be standardized to have a mean of zero and a standard deviation of one. Defaults to FALSE.

Logical. If TRUE, enables hybrid-VAR models where both directed contemporaneous paths and contemporaneous covariances among residuals are candidate relations in the search space. Defaults to FALSE.

Logical. If true, VAR models where contemporaneous covariances among residuals are candidate relations in the search space. Defaults to FALSE.

A list containing information created in setup().

A list containing group-level information. NULL in aggSEM and indSEM.

A list containing individual- and (potentially) subgroup-level information.

Chi square cutoff, .05 level adjusted for multiple tests.

Z score cutoff, .05 level adjusted for multiple tests.

A list containing information created in setup().

A list containing group-level information. NULL in aggSEM and indSEM.

A list containing individual- and (potentially) subgroup-level information.

A list of z values across individuals.

A character vector containing eligible paths that gimme is allowed to drop from the model at a given stage.

The proportion of individuals for whom a path must be nonsignificant in order for it to be dropped from the models. NULL if used at the individual-level.

The number of subjects in a given stage of the search. If in the group stage, n_subj equals the number of subjects. If in the subgroup stage, n_subj equals the number of individuals in a given subgroup. At the individual stage, n_subj = 1.

Z score cutoff for significance testing.

A fitted gimme object.

A character vector containing syntax that never changes.

A character vector containing syntax that does not change in a given stage of pruning.

A character vector containing the syntax that is allowed to change in a given stage of pruning.

A list of datasets to be used in a given stage of the search. Varies based on group, subgroup, or individual-level stage.

The number of paths that are eligible for pruning. Equal to the number of paths in add_syntax.

The number of subjects in a given stage of the search. If in the group stage, n_subj equals the number of subjects. If in the subgroup stage, n_subj equals the number of individuals in a given subgroup. At the individual stage, n_subj = 1.

The proportion of individuals for whom a path must be nonsignificant in order for it to be dropped from the models. NULL if used at the individual-level.

A character vector containing eligible paths that gimme is allowed to drop from the model at a given stage.

Logical. Only present in order to instruct gimme what message to print to console using writeLines.

Z score cutoff for significance testing.

The vector of variable names to be recoded

A vector containing the latent variable names used internally.

A vector containing the observed variable names, either provided by the user (as a header) or provided by R (e.g., V1, V2).

A fitted gimme object.

The number of lags tested in the Box-Pierce and Ljung-Box tests of the residuals. If user does not specify a value, default is the smaller of 10 or the length of the time series divided by 5.

An object from lavaan.

An object from lavaan.

eligable paths at this stage. For subgrouping, group and fixed paths. For pruning, only group paths.

The data to be used to estimate response function

A vector containing '0' when the stimuli of interest is not present and '1' otherwise. Number of observations across time must equal the length of data.

Time between observations; for fMRI this is the repetition time. Defaults to 1.

A character vector containing syntax that never changes.

A character vector containing syntax that does not change in a given stage of searching.

A character vector containing the syntax that is allowed to change in a given stage of searching.

The number of paths present in a given stage of searching. Equal to the number of paths in add_syntax.

A list of datasets to be used in a given stage of the search. Varies based on group, subgroup, or individual-level stage.

A character vector containing eligible paths that gimme is allowed to add to the model at a given stage.

The proportion of individuals for whom a path must be nonsignificant in order for it to be dropped from the models. NULL if used at the individual-level.

The number of subjects in a given stage of the search. If in the group stage, n_subj equals the number of subjects. If in the subgroup stage, n_subj equals the number of individuals in a given subgroup. At the individual stage, n_subj = 1.

Cutoff used in order for MI to be considered significant. Value varies depending on stage of search (e.g., group, subgroup, individual).

Logical. Only present in order to instruct gimme what message to print to console using writeLines.

Object created at beginning of gimme containing static info.

Which individual this is.

A list of datasets to be used in a given stage of the search. Varies based on group, subgroup, or individual-level stage.

A character vector containing syntax that never changes.

A character vector containing syntax that does not change in a given stage of searching.

A character vector containing eligible paths that gimme is allowed to add to the model at a given stage.

The proportion of individuals for whom a path must be nonsignificant in order for it to be dropped from the models. NULL if used at the individual-level.

The number of subjects in a given stage of the search. If in the group stage, n_subj equals the number of subjects. If in the subgroup stage, n_subj equals the number of individuals in a given subgroup. At the individual stage, n_subj = 1.

Cutoff used in order for MI to be considered significant. Value varies depending on stage of search (e.g., group, subgroup, individual).

Logical. Only present in order to instruct gimme what message to print to console using writeLines.

A list of datasets to be used in a given stage of the search. Varies based on group, subgroup, or individual-level stage.

A character vector containing syntax that never changes.

A character vector containing syntax that does not change in a given stage of searching.

A character vector containing eligible paths that gimme is allowed to add to the model at a given stage.

The proportion of individuals for whom a path must be nonsignificant in order for it to be dropped from the models. NULL if used at the individual-level.

The number of subjects in a given stage of the search. If in the group stage, n_subj equals the number of subjects. If in the subgroup stage, n_subj equals the number of individuals in a given subgroup. At the individual stage, n_subj = 1.

Cutoff used in order for MI to be considered significant. Value varies depending on stage of search (e.g., group, subgroup, individual).

Logical. Only present in order to instruct gimme what message to print to console using writeLines.

a list of dataframes.

a list of variable sets. Contains varLabels$coln, all column names, varLabels$conv, the names of variables to convolve, and varLabels$exog, a list of exogenous variables (if any).

Expected response length in seconds. For functional MRI BOLD, 16 seconds (default) is typical for the hemodynamic response function.

Interval between data acquisition. Currently must be a scalar For fMRI studies, this is the repetition time. Defaults to 1.

a list or directory.

a lit of control options.

lavaan-style syntax containing paths with which to begin model estimation (optional). That is, Y~X indicates that Y is regressed on X, or X predicts Y. If no header is used, then variables should be referred to with V followed (with no separation) by the column number. If a header is used, variables should be referred to using variable names. To reference lag variables, "lag" should be added to the end of the variable name with no separation. Defaults to NULL.

a list or directory

Variable labels.

List used in setup function.

A matrix (for no subgroups) or list of A matrices, with slice # = # of subgroups.

Phi matrix (for no subgroups) or list of Phi matrices, with slice # = # of subgroups.

matrix (for no subgroups) or list of Psi matrices, with slice # = # of subgroups.

Optional vector of length N that indicates which subgroup each individual is in.

Number of indvidiuals.

Defaults to "random" for ind level paths, with 50 percent chance of positive and 50 percent negative, other option is either "neg" or "pos" which provides all negative or all positive relations, respectively.

Defaults to "random" for ind level paths, with 50 percent chance of positive and 50 percent negative, other option is either "neg" or "pos" which provides all negative or all positive relations, respectively.

Number of observations (T) per individual. Burn in of 400 is used to generate then discarded.

Sparsity of individual-level A paths. 0 indicates no individual-level. Use decimals. Default is 0.01, meaning that each path that is not in the group-level A matrix has a 0.01 chance of being added.

Sparsity of individual-level Phi paths. 0 indicates no individual-level. Use decimals. Default is 0.01, meaning that each path that is not in the group-level Phi matrix has a 0.01 chance of being added.

Sparsity of individual-level Psi paths. 0 indicates no individual-level. Use decimals. Default is 0, meaning that each path that is not in the group-level Psi matrix has a 0 chance of being added at the ind. level. Individual- level paths added at this rate per individual.

A fitted gimme object.

A character vector indicating what levels of the solution tree you would like returned. Options are "group", "individual", or c("group", "individual"). Defaults to c("group", "individual").

A character vector indicating additional information to include in tree plot. Options include "stage", "pruned", "rmsea", "nnfi", "cfi","srmr", "grp_sol", "bic", "aic", "modularity." Defaults to NULL.

A character vector indicating the names of subjects to print. Defaults to "all."

Logical. If TRUE, plot of tree is produced. Defaults to FALSE.

A list containing information created in setup().

A list containing group-level information. NULL in aggSEM and indSEM.

A list containing output from indiv.search().

A list containing subgroup information.

A list containing information specific to each subgroup.

The coefficient matrix from an individual