Welcome to SEMinR

SEMinR allows users to easily create and modify structural equation models (SEM). It allows estimation using either covariance-based SEM (CBSEM, such as LISREL/Lavaan), or Partial Least Squares Path Modeling (PLS-PM, such as SmartPLS/semPLS).

Main features of using SEMinR:

• A natural feeling, domain-specific language to build and estimate SEMs in R
• High-level functions to quickly specify interactions and complicated structural models
• Modular design of models that promotes reuse of model components
• Encourages best practices by use of smart defaults and warnings

Take a look at the easy syntax and modular design:

# Define measurements with famliar terms: reflective, composite, multi-item constructs, etc.
measurements <- constructs(
  reflective("Image",       multi_items("IMAG", 1:5)),
  composite("Expectation", multi_items("CUEX", 1:3)),
  composite("Loyalty",     multi_items("CUSL", 1:3), weights = mode_B),
  composite("Complaints",  single_item("CUSCO"))
)

# Create four relationships (two regressions) in one line!
structure <- relationships(
  paths(from = c("Image", "Expectation"), to = c("Complaints", "Loyalty"))
)

# Estimate the model using PLS estimation scheme (Consistent PLS for reflectives)
pls_model <- estimate_pls(data = mobi, measurements, structure)

# Re-estimate the model as a purely reflective model using CBSEM
cbsem_model <- estimate_cbsem(data = mobi, as.reflective(measurements), structure)