Adaptive testing design and governance

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Native adaptive-design utilities deliberately remain simple and auditable: item-bank declaration, maximum-information selection, exposure summaries, content-balance audits, stopping rules, and step-by-step adaptive traces.

items <- data.frame(item_id=paste0("I",1:6),a=c(.8,1,1.5,1.1,.9,1.2),b=seq(-1.5,1.5,length.out=6),c=0,d=1)
bank <- eyeprocess_irt_item_bank(items, content = c("A","A","B","B","C","C"))
eyeprocess_irt_item_selection(bank, theta = 0)
#> $selected
#> [1] "I3"
#> 
#> $information
#> [1] 0.5349577
#> 
#> $theta
#> [1] 0
#> 
#> $reason
#> [1] "maximum_information"
#> 
#> $candidate_count
#> [1] 6
#> 
#> attr(,"class")
#> [1] "eye_irt_item_selection"
eyeprocess_irt_stopping_rule(10, se = .25)
#> $stop
#> [1] TRUE
#> 
#> $reason
#> [1] "target_precision"
#> 
#> $n_administered
#> [1] 10
#> 
#> $se
#> [1] 0.25

For mature CAT simulation and constrained/shadow-testing designs, eyeprocess delegates to catR and mirtCAT. Availability is explicit and no simpler CAT engine is silently substituted.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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