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Video 7 Chapter 0 seminrExtras

seminrExtras 1.0.0 — The Advanced PLS-SEM Toolkit in R

seminrExtras is a companion package for seminr that ships the advanced PLS-SEM assessment methods that don't fit the base workflow — COA, NCA, NCA-ESSE, cIPMA, FIMIX-PLS, PLS-POS, CTA-PLS, PCM, CVPAT, and congruence testing. This tutorial links to a fully runnable walkthrough on the MOBI and corporate reputation datasets.

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What you'll learn

  • Install seminrExtras from CRAN and load it alongside seminr
  • Diagnose out-of-sample overfitting with Composite Overfit Analysis (assess_coa)
  • Test necessity with Necessary Condition Analysis and its Effect Size Sensitivity Extension (assess_nca, assess_nca_esse)
  • Build Combined Importance-Performance Maps that overlay NCA necessity (assess_cipma)
  • Uncover unobserved heterogeneity with FIMIX-PLS and PLS-POS (assess_fimix, assess_pos)
  • Confirm reflective measurement with CTA-PLS (assess_cta)
  • Evaluate the predictive contribution of mediators with PCM (assess_pcm)
  • Compare predictive ability across models with CVPAT (assess_cvpat_compare)

R practice in this video

  • > Always seed your bootstraps and cross-validation folds with seed = 123
  • > Stay in R for the full assessment workflow — one language, reproducible pipeline
  • > Open issues on GitHub when something surprises you — it helps every future user

Overview

seminrExtras is a companion package for seminr that ships the advanced PLS-SEM assessment methods that don’t fit into the base seminr workflow — the things you usually end up writing custom scripts for, or reaching for another software package for.

The goal is simple: if you already use SEMinR to estimate your PLS-SEM models, you should be able to stay in R for the full modern toolkit of prediction, necessity, heterogeneity, measurement confirmation, and overfit diagnostics.

Version 1.0.0 is now on CRAN.

install.packages("seminrExtras")
library(seminrExtras)

Requires seminr ≥ 2.4.0.

Full runnable walkthrough

The runnable demo document walks through every feature end-to-end on the built-in MOBI customer satisfaction and corporate reputation datasets. A knitted version with all code output and plots is hosted here; the source .Rmd is also available for you to run locally.

To run it yourself:

download.file(
  "https://nicholasdanks.com/learn/seminrExtras_1_0_demo.Rmd",
  "seminrExtras_1_0_demo.Rmd"
)
rmarkdown::render("seminrExtras_1_0_demo.Rmd")

What’s in 1.0.0

Composite Overfit Analysis (COA)

assess_coa() diagnoses why and for whom your PLS model fails to generalise out-of-sample. Predictive deviance, deviance trees, and unstable paths in one call.

assess_coa(model, focal_construct = "CUSL", noFolds = 10, seed = 123)

Necessary Condition Analysis (NCA)

assess_nca() runs NCA with fully internal CE-FDH and CR-FDH ceiling lines. No external NCA package dependency.

assess_nca(model, target = "Satisfaction", test.rep = 1000, seed = 123)

NCA-ESSE (Effect Size Sensitivity Extension)

assess_nca_esse() implements Becker et al. (2026) — varies the ECDF threshold to test how robust NCA conclusions are to extreme-response observations.

assess_nca_esse(model, target = "Satisfaction",
                thresholds = seq(0, 0.05, by = 0.005), seed = 123)

Combined IPMA (cIPMA)

assess_cipma() overlays NCA necessity onto the IPMA map. assess_ipma() is the IPMA-only convenience wrapper. Supports higher-order constructs, mediation, and moderation.

assess_cipma(model, target = "Loyalty", scale_min = 1, scale_max = 10,
             nca_test.rep = 1000, seed = 123)

FIMIX-PLS

assess_fimix() and assess_fimix_compare() implement EM-based latent-class segmentation with multi-start initialisation and information-criteria comparison across K.

assess_fimix_compare(model, K_range = 2:4, nstart = 10, seed = 123)

PLS-POS

assess_pos(), assess_pos_compare(), and pos_segments() implement prediction-oriented segmentation (Becker et al., 2013) that maximises ΣR² across segments — no distributional assumptions.

assess_pos_compare(model, K_range = 2:4, nstart = 10, max_iter = 100, seed = 123)

CTA-PLS

assess_cta() runs confirmatory tetrad analysis with automatic indicator-borrowing for constructs with fewer than 4 indicators (Gudergan et al., 2008).

assess_cta(model, nboot = 5000, seed = 123)

PCM (Predictive Contribution of the Mediator)

assess_pcm() evaluates whether a mediator actually improves out-of-sample prediction by comparing Direct-Antecedent and Earliest-Antecedent predictions on the isolated mediation sub-model (Danks, 2021). Mediation paths are auto-detected.

assess_pcm(model, target = "Loyalty", noFolds = 10, reps = 10)

CVPAT and congruence testing

assess_cvpat(), assess_cvpat_compare(), and congruence_test() round out the prediction and weight-stability toolkit.

assess_cvpat_compare(established_model, alternative_model,
                     nboot = 2000, noFolds = 10, reps = 10, seed = 123)

Every feature ships with

  • print(), summary(), and plot() S3 methods
  • A runnable demo("seminr-pls-<feature>") inside the package
  • A 740+ test suite backing the whole codebase

Feedback

If you hit something that doesn’t work the way you expect, or want a method added, please open an issue: github.com/sem-in-r/seminrExtras/issues. That’s the fastest way to get it on the roadmap, and it helps every future user.

Acknowledgements

Huge thanks to Soumya Ray and the SEMinR team, and to Christian Ringle, José Luis Roldán, and Marko Sarstedt for their support and guidance along the way.

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