Package: pwSEM 1.0.0

pwSEM: Piecewise Structural Equation Modelling

Conduct dsep tests (piecewise SEM) of a directed, or mixed, acyclic graph without latent variables (but possibly with implicitly marginalized or conditioned latent variables that create dependent errors) based on linear, generalized linear, or additive modelswith or without a nesting structure for the data. Also included are functions to do desp tests step-by-step,exploratory path analysis, and Monte Carlo X2 probabilities. This package accompanies Shipley, B, (2026).Cause and Correlation in Biology: A User's Guide to Path Analysis, StructuralEquations and Causal Inference (3rd edition). Cambridge University Press.

Authors:Bill Shipley [aut, cre]

pwSEM_1.0.0.tar.gz
pwSEM_1.0.0.zip(r-4.7-any)pwSEM_1.0.0.zip(r-4.6-any)pwSEM_1.0.0.zip(r-4.5-any)
pwSEM_1.0.0.tgz(r-4.6-any)pwSEM_1.0.0.tgz(r-4.5-any)
pwSEM_1.0.0.tar.gz(r-4.7-any)pwSEM_1.0.0.tar.gz(r-4.6-any)
pwSEM_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
pwSEM/json (API)

# Install 'pwSEM' in R:

install.packages('pwSEM', repos = c('https://billshipley.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/billshipley/pwsem/issues

Datasets:

nested_data - Nested_data:
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

On CRAN:

4.00 score 2 stars 5 scripts 268 downloads 11 exports 41 dependencies

Last updated from:6722a96a6b. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	159
source / vignettes	OK	240
linux-release	OK	140
macos-release	OK	203
macos-oldrel	OK	214
windows-devel	OK	87
windows-release	OK	105
windows-oldrel	OK	102
wasm-release	OK	102

Exports:basiSet.MAG CI.algorithm DAG.to.MAG.in.pwSEM generalized.covariance get.AIC MAG.to.DAG.in.pwSEM MCX2 perm.generalized.covariance pwSEM vanishing.tetrads view.paths

Dependencies:ADGofTest BiocGenerics BiocManager boot cli cluster colorspace copula cpp11 gamm4 generics ggm glue graph gsl igraph lattice lifecycle lme4 magrittr MASS mathjaxr Matrix mgcv minqa mvtnorm nlme nloptr numDeriv pcaPP pkgconfig poolr pspline rbibutils Rcpp RcppEigen Rdpack reformulas rlang stabledist vctrs

pwSEM

Introduction | Some technical details | The pwSEM function | data structure | Example 1: dependent endogenous errors, normally distributed variables and no nesting structure in the data | Example 2: dependent endogenous errors from marginalized latents, Poisson distributed variables and no nesting structure in the data | Example 3: dependent errors involving endogenous variables, normally-distributed data and with a 2-level grouping structure and using smoothing splines for the d-separation tests | DAG.to.MAG.in.pwSEM | MAG.to.DAG.in.pwSEM | generalized covariance function and its permutation version | The view.paths function | The basiSet.MAG function | The get.AIC function | The MCX2 function | The CI.algorithm() function | The vanishing.tetrads function

Rendered frompwSEM.Rmdusingknitr::rmarkdown

Last update: 2025-06-27
Started: 2024-02-07

Help page	Topics
basiSet.MAG	basiSet.MAG
The CI.algorithm function	CI.algorithm
Title DAG.to.MAG.in.pwSEM	DAG.to.MAG.in.pwSEM
Generalized covariance function	generalized.covariance
Title get.AIC	get.AIC
Title MAG.to.DAG.in.pwSEM	MAG.to.DAG.in.pwSEM
Title Monte Carlo chi-square (MCX2)	MCX2
nested_data:	nested_data
perm.generalized.covariance	perm.generalized.covariance
The pwSEM function	pwSEM
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.no.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_normal.with.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_poisson.no.nesting
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	sim_tetrads
Summary Method for pwSEM Class	summary.pwSEM.class
The vanishing.tetrads function	vanishing.tetrads
view.paths	view.paths

Package: pwSEM 1.0.0

pwSEM: Piecewise Structural Equation Modelling

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