| 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