R: Piecewise Structural Equation Modelling

# Piecewise Structural Equation Modelling

## Documentation for package ‘pwSEM’ version 1.0.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

basiSet.MAG | basiSet.MAG
CI.algorithm | The CI.algorithm function
DAG.to.MAG.in.pwSEM | Title DAG.to.MAG.in.pwSEM
generalized.covariance | Generalized covariance function
get.AIC | Title get.AIC
MAG.to.DAG.in.pwSEM | Title MAG.to.DAG.in.pwSEM
MCX2 | Title Monte Carlo chi-square (MCX2)
nested_data | nested_data:
perm.generalized.covariance | perm.generalized.covariance
pwSEM | The pwSEM function
sim_normal.no.nesting | sim_normal.no.nesting Simulated data with correlated errors involving endogenous variables, normally-distributed data and without any grouping structure Data generated using this mixed acyclic graph: X1->X2->X3->X4 and X2<->X4
sim_normal.with.nesting | sim_normal.with.nesting: Simulated data with correlated errors involving endogenous variables, normally-distributed data and without any grouping structure Data generated using this mixed acyclic graph: X1->X2->X3->X4 and X2<->X4
sim_poisson.no.nesting | sim_poisson.no.nesting: Simulated data with correlated errors involving endogenous variables, Poisson-distributed data and without any grouping structure Data generated using this mixed acyclic graph: X1->X2->X3->X4 and X2<->X4
sim_tetrads | sim_tetrads: Simulated data to be used with the vanishing.tetrads function Data generated using this directed acyclic graph, with L being latent: L->X1, L->X2, L->X3->X4
summary.pwSEM.class | Summary Method for pwSEM Class
vanishing.tetrads | The vanishing.tetrads function
view.paths | view.paths