Introducing seminrExtras

seminrExtras 1.0.0 is now on CRAN — a companion package for SEMinR that brings the advanced PLS-SEM assessment toolkit (prediction, necessity, heterogeneity, measurement confirmation, and overfit diagnostics) into R.

Author

Nicholas Danks

Published

April 21, 2026

SEMinR

Introducing seminrExtras — now on CRAN at 1.0.0. 🎉 I’ve never properly announced this package to the community, so: here it is.

What it is. 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.

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 + unstable paths in one call.
  • 📏 Necessary Condition Analysis (NCA) — assess_nca() with fully internal CE-FDH and CR-FDH ceiling lines. No external NCA dependency.
  • 🧪 NCA-ESSE — assess_nca_esse() implements the Effect Size Sensitivity Extension (Becker et al., 2026) — tests how robust your NCA conclusions are to extreme responses.
  • 🗺️ Combined IPMA (cIPMA) — assess_cipma() overlays NCA necessity onto the IPMA map. assess_ipma() for IPMA-only. Supports HOCs, mediation, and moderation.
  • 🧬 FIMIX-PLS — assess_fimix() and assess_fimix_compare(). EM-based latent-class segmentation with multi-start init and IC comparison across K.
  • 🎯 PLS-POS — assess_pos(), assess_pos_compare(), pos_segments(). Prediction-oriented segmentation that maximises ΣR² across segments (Becker et al., 2013) — no distributional assumptions.
  • 📐 CTA-PLS — assess_cta() for confirmatory tetrad analysis. Automatic indicator-borrowing for constructs with fewer than 4 indicators (Gudergan et al., 2008).
  • 🔗 PCM (Predictive Contribution of the Mediator) — assess_pcm() evaluates whether a mediator actually improves out-of-sample prediction (Danks, 2021). Auto-detects mediation paths.
  • ✅ CVPAT + congruence testing — assess_cvpat(), assess_cvpat_compare(), and congruence_test() round out the prediction and weight-stability toolkit.

Every feature has print(), summary(), and plot() S3 methods, and a runnable demo("seminr-pls-<feature>").

Get it

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

Requires seminr ≥ 2.4.0.

Source, docs, and issues: https://github.com/sem-in-r/seminrExtras

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

I’ve also put together a single runnable demo document that walks through every new feature end-to-end on the built-in corporate reputation and MOBI datasets: https://gist.github.com/NicholasDanks/1dddab452c4fdbbce061a93d6a83aed1

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.