Composite Overfit Analysis (COA):
assess_coa(), predictive_deviance(),
deviance_tree(), unstable_params(),
group_rules(), competes() for diagnosing
why and for whom PLS models fail to generalise
out-of-sample.
Necessary Condition Analysis (NCA):
assess_nca() with fully internal CE-FDH and CR-FDH
algorithms (no external NCA package dependency).
NCA-ESSE: assess_nca_esse()
implements the effect size sensitivity extension (Becker et al.,
2026).
Combined Importance-Performance Map Analysis
(cIPMA): assess_cipma() integrates IPMA with NCA
to classify constructs into actionable priority quadrants.
assess_ipma() provides an IPMA-only convenience wrapper.
Supports HOC, mediation, and moderation models.
FIMIX-PLS: assess_fimix() and
assess_fimix_compare() for EM-based latent class
segmentation with multi-start initialisation and information criteria
comparison.
PLS-POS: assess_pos(),
assess_pos_compare(), and pos_segments() for
prediction-oriented segmentation that maximises the sum of R-squared
across segments (Becker et al., 2013).
CTA-PLS: assess_cta() for
confirmatory tetrad analysis with automatic indicator borrowing for
constructs with fewer than 4 indicators (Gudergan et al.,
2008).
Predictive Contribution of the Mediator (PCM):
assess_pcm() evaluates whether a mediating construct
improves out-of-sample prediction by comparing DA and EA approaches on
isolated sub-models (Danks, 2021).
assess_cvpat() and
assess_cvpat_compare(): fixed bootstrap test branches, loss
function return types, and reference metadata.
congruence_test(): fixed division guard,
upper-triangular masking, and bootstrap robustness for
nboot = 0.
All features include print(),
summary(), and plot() S3 methods.
Comprehensive test suite (740+ tests).
Demo files for all features:
demo("seminr-pls-<feature>").
Updated vignette with examples for all features.
seminr >= 2.4.0.rpart added to Imports (for COA deviance trees).MASS, paran, psych,
learnr added to Suggests.