Piecewise Structural Equation Modelling

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Documentation for package ‘pwSEM’ version 1.0.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

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