Modeling Moderated Networks
Bootstrapping network estimation for moderated networks
Descriptive statistics for bootNet
objects
Node centrality, clustering coefficients, and shortest path lengths
Create table of centrality values or clustering coefficients
Plots for node centrality values or clustering coefficients
Compare two to three lmerVAR
models
Conditional effects plot
Fit cross-sectional and idiographic moderated network models
Provides model coefficients with confidence intervals
Plot confidence intervals for interaction terms
Mixed-effects modeling for the GVAR in multilevel data
Log-likelihood functions and Likelihood Ratio Tests for moderated netw...
Fit GVAR models with multilevel data
Main workhorse for simulating VAR and mlGVAR data
Power simulator for cross-sectional and idiographic networks
modnets: Modeling Moderated Networks
Select a model based on output from resample
Get adjacency matrices from fit objects
Convert continuous variables into ordinal variables
Plot method for output of resample function
Plot bootNet
outputs
Plot model coefficients with confidence intervals
Plot conditional networks at different levels of the moderator
Plot moderated and unmoderated network models
Plot temporal and contemporaneous networks in the same window
Plot temporal, contemporaneous, and between-subject networks
Plot results of power simulations
Plot the ECDF of p-values from resampling
Plot stability selection paths for a given outcome
Calculate prediction error from network models
Bootstrapping or multi-sample splits for variable selection
Reports the minimum sample size required to fit a network model
Shows which variables were selected for each node of a network
Simulate network structure and data
Descriptive statistics for power simulation results
Fit SUR models with or without constraints
Creates temporal and contemporaneous network of SUR results
Variable selection for moderated networks
Methods for modeling moderator variables in cross-sectional, temporal, and multi-level networks. Includes model selection techniques and a variety of plotting functions. Implements the methods described by Swanson (2020) <https://www.proquest.com/openview/d151ab6b93ad47e3f0d5e59d7b6fd3d3>.