psychonetrics-package {psychonetrics} | R Documentation |
Structural Equation Modeling and Confirmatory Network Analysis
Description
Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data <doi:10.31234/osf.io/8ha93>. Allows for confirmatory testing and fit as well as exploratory model search.
Details
The DESCRIPTION file:
Package: | psychonetrics |
Type: | Package |
Title: | Structural Equation Modeling and Confirmatory Network Analysis |
Version: | 0.13 |
Author: | Sacha Epskamp |
Maintainer: | Sacha Epskamp <mail@sachaepskamp.com> |
Description: | Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data <doi:10.31234/osf.io/8ha93>. Allows for confirmatory testing and fit as well as exploratory model search. |
License: | GPL-2 |
LinkingTo: | Rcpp (>= 0.11.3), RcppArmadillo, pbv, roptim |
Depends: | R (>= 4.3.0) |
Imports: | methods, qgraph, numDeriv, dplyr, abind, Matrix (>= 1.6-5), lavaan, corpcor, glasso, mgcv, optimx, VCA, pbapply, parallel, magrittr, IsingSampler, tidyr, psych, GA, combinat, rlang |
Suggests: | psychTools, semPlot, graphicalVAR, metaSEM, mvtnorm, ggplot2 |
ByteCompile: | true |
URL: | http://psychonetrics.org/ |
BugReports: | https://github.com/SachaEpskamp/psychonetrics/issues |
StagedInstall: | true |
NeedsCompilation: | yes |
Archs: | x64 |
Index of help topics:
CIplot Plot Analytic Confidence Intervals Ising Ising model Jonas Jonas dataset MIs Print modification indices StarWars Star Wars dataset addMIs Model updating functions aggregate_bootstraps Aggregate Bootstrapped Models bifactor Bi-factor models bootstrap Bootstrap a psychonetrics model changedata Change the data of a psychonetrics object checkJacobian Diagnostic functions compare Model comparison covML Maximum likelihood covariance estimate dlvm1 Lag-1 dynamic latent variable model family of psychonetrics models for panel data duplicationMatrix Model matrices used in derivatives emergencystart Reset starting values to simple defaults esa Ergodic Subspace Analysis factorscores Compute factor scores fit Print fit indices fixpar Parameters modification fixstart Attempt to Fix Starting Values generate Generate data from a fitted psychonetrics object getVCOV Obtain the asymptotic covariance matrix getmatrix Extract an estimated matrix groupequal Group equality constrains latentgrowth Latnet growth curve model logbook Retrieve the psychonetrics logbook lvm Continuous latent variable family of psychonetrics models meta_varcov Variance-covariance and GGM meta analysis ml_lvm Multi-level latent variable model family ml_tsdlvm1 Multi-level Lag-1 dynamic latent variable model family of psychonetrics models for time-series data modelsearch Stepwise model search parameters Print parameter estimates parequal Set equality constrains across parameters partialprune Partial pruning of multi-group models prune Stepdown model search by pruning non-significant parameters. psychonetrics-class Class '"psychonetrics"' psychonetrics-package Structural Equation Modeling and Confirmatory Network Analysis psychonetrics_bootstrap-class Class '"psychonetrics_bootstrap"' psychonetrics_log-class Class '"psychonetrics"' runmodel Run a psychonetrics model setestimator Convenience functions setverbose Should messages of computation progress be printed? simplestructure Generate factor loadings matrix with simple structure stepup Stepup model search along modification indices transmod Transform between model types tsdlvm1 Lag-1 dynamic latent variable model family of psychonetrics models for time-series data unionmodel Unify models across groups var1 Lag-1 vector autoregression family of psychonetrics models varcov Variance-covariance family of psychonetrics models
This package can be used to perform Structural Equation Modeling and confirmatory network modeling. Current implemented families of models are (1) the variance–covariance matrix (varcov
), (2) the latent variable model (lvm
), (3) the lag-1 vector autoregression model (var1
), and (4) the dynamical lag-1 latent variable model for panel data (dlvm1
) and for time-series data (tsdlvm1
).
Author(s)
Sacha Epskamp
Maintainer: Sacha Epskamp <mail@sachaepskamp.com>
References
More information: psychonetrics.org