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.

Latent Variable Analysis

Fit a variety of latent variable models, including confirmatory factor analysis, structural equation modeling and latent growth curve models.

Identifiability of Linear Structural Equation Models

Provides routines to check identifiability of linear structural equation models and factor analysis models. The routines are based on the graphical representation of structural equation models.

Building and Estimating Structural Equation Models

A powerful, easy to use syntax for specifying and estimating complex Structural Equation Models. Models can be estimated using Partial Least Squares Path Modeling or Covariance-Based Structural Equation Modeling or covariance based Confirmatory Factor Analysis (Ray, Danks, and Valdez 2021 ).

Case Influence in Structural Equation Models

A set of tools for evaluating several measures of case influence for structural equation models.

Helper Functions for Structural Equation Modeling

An assortment of helper functions for doing structural equation modeling, mainly by 'lavaan' for now. Most of them are time-saving functions for common tasks in doing structural equation modeling and reading the output. This package is not for functions that implement advanced statistical procedures. It is a light-weight package for simple functions that do simple tasks conveniently, with as few dependencies as possible.

Spatially Explicit Structural Equation Modeling

Structural equation modeling is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with inter-correlated dependent and independent variables. Here we implement a simple method for spatially explicit structural equation modeling based on the analysis of variance co-variance matrices calculated across a range of lag distances. This method provides readily interpreted plots of the change in path coefficients across scale.

Bootstrapping Helpers for Structural Equation Modelling

A collection of helper functions for forming bootstrapping confidence intervals and examining bootstrap estimates in structural equation modelling. Currently supports models fitted by the 'lavaan' package by Rosseel (2012) .

Symbolic Computation for Structural Equation Models

A collection of functions for symbolic computation using the 'caracas' package for structural equation models and other statistical analyses. Among its features is the ability to calculate the model-implied covariance (and correlation) matrix and the sampling covariance matrix of variable functions using the delta method.

Influential Cases in Structural Equation Modeling

Sensitivity analysis in structural equation modeling using influence measures and diagnostic plots. Support leave-one-out casewise sensitivity analysis presented by Pek and MacCallum (2011) and approximate casewise influence using scores and casewise likelihood.