JuliaPsychometricsBazaar · frankier · Jul 4, 2025 · Apr 20, 2025 · Apr 20, 2025 · May 29, 2025
diff --git a/.gitignore b/.gitignore
@@ -2,12 +2,9 @@
 *.jl.cov
 *.jl.mem
 Manifest.toml
-!/docs/Manifest.toml
-!/test/Manifest.toml
 !/binder/Manifest.toml
-/attic/
+attic/
 /.vscode/
-/docs/attic/
 /docs/build/
 /docs/.CondaPkg/
 /docs/LocalPreferences.toml

diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "ComputerAdaptiveTesting"
 uuid = "5a0d4f34-1f62-4a66-80fe-87aba0485488"
 authors = ["Frankie Robertson"]
-version = "0.3.2"
+version = "0.4.0"
 
 [deps]
 Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
@@ -11,6 +11,7 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 EffectSizes = "e248de7e-9197-5860-972e-353a2af44d75"
+ElasticArrays = "fdbdab4c-e67f-52f5-8c3f-e7b388dad3d4"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 FittedItemBanks = "3f797b09-34e4-41d7-acf6-3302ae3248a5"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
@@ -23,6 +24,7 @@ MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 Mmap = "a63ad114-7e13-5084-954f-fe012c677804"
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
 PsychometricsBazaarBase = "b0d9cada-d963-45e9-a4c6-4746243987f1"
+QuickHeaps = "30b38841-0f52-47f8-a5f8-18d5d4064379"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
@@ -46,8 +48,9 @@ DataFrames = "1.6.1"
 Distributions = "^0.25.88"
 DocStringExtensions = " ^0.9"
 EffectSizes = "^1.0.1"
+ElasticArrays = "1.2.12"
 FillArrays = "0.13, 1.5.0"
-FittedItemBanks = "^0.6.3"
+FittedItemBanks = "^0.7.2"
 ForwardDiff = "1"
 HypothesisTests = "^0.10.12, ^0.11.0"
 Interpolations = "^0.14, ^0.15"
@@ -59,10 +62,10 @@ MacroTools = "^0.5.6"
 Mmap = "^1.11"
 Optim = "1.7.3"
 PrecompileTools = "1.2.1"
-PsychometricsBazaarBase = "^0.8.1"
+PsychometricsBazaarBase = "^0.8.4"
+QuickHeaps = "0.2.2"
 Random = "^1.11"
 Reexport = "1"
-ResumableFunctions = "^0.6"
 Setfield = "^1"
 SparseArrays = "^1.11"
 StaticArrays = "1"
@@ -75,8 +78,7 @@ julia = "^1.11"
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
-ResumableFunctions = "c5292f4c-5179-55e1-98c5-05642aab7184"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Aqua", "JET", "Optim", "ResumableFunctions", "Test"]
+test = ["Aqua", "JET", "Optim", "Test"]
diff --git a/benchmark/benchmarks.jl b/benchmark/benchmarks.jl
@@ -8,7 +8,6 @@ using FittedItemBanks.DummyData: dummy_full, SimpleItemBankSpec, StdModel4PL
 using ComputerAdaptiveTesting.Aggregators
 using PsychometricsBazaarBase.Optimizers
 using PsychometricsBazaarBase.Integrators: even_grid
-using ComputerAdaptiveTesting.NextItemRules: mirtcat_quadpts
 using ComputerAdaptiveTesting.NextItemRules: ExpectationBasedItemCriterion,
                                              PointResponseExpectation
 using ComputerAdaptiveTesting.NextItemRules
@@ -27,21 +26,21 @@ function prepare_4pls(group)
         num_questions = 20,
         num_testees = 1
     )
-    integrator = even_grid(-6.0, 6.0, mirtcat_quadpts(1))
+    integrator = even_grid(-6.0, 6.0, 121)
     optimizer = AbilityOptimizer(OneDimOptimOptimizer(-6.0, 6.0, NelderMead()))
 
-    dist_ability_estimator = PriorAbilityEstimator()
+    dist_ability_estimator = PosteriorAbilityEstimator()
     ability_estimators = [
         ("mean", MeanAbilityEstimator(dist_ability_estimator, integrator)),
         ("mode", ModeAbilityEstimator(dist_ability_estimator, optimizer))
     ]
     response_idxs = sample(rng, 1:20, 10)
 
     for (est_nick, ability_estimator) in ability_estimators
-        next_item_rule = ItemStrategyNextItemRule(
+        next_item_rule = ItemCriterionRule(
             ExhaustiveSearch(),
             ExpectationBasedItemCriterion(PointResponseExpectation(ability_estimator),
-                AbilityVarianceStateCriterion(
+                AbilityVariance(
                     integrator, distribution_estimator(ability_estimator)))
         )
         next_item_rule = preallocate(next_item_rule)

diff --git a/docs/examples/examples/ability_convergence_3pl.jl b/docs/examples/examples/ability_convergence_3pl.jl
@@ -20,9 +20,9 @@ using Distributions: Normal, cdf
 using AlgebraOfGraphics
 using ComputerAdaptiveTesting
 using ComputerAdaptiveTesting.Sim: auto_responder
-using ComputerAdaptiveTesting.NextItemRules: AbilityVarianceStateCriterion
-using ComputerAdaptiveTesting.TerminationConditions: FixedItemsTerminationCondition
-using ComputerAdaptiveTesting.Aggregators: PriorAbilityEstimator,
+using ComputerAdaptiveTesting.NextItemRules: AbilityVariance
+using ComputerAdaptiveTesting.TerminationConditions: FixedLength
+using ComputerAdaptiveTesting.Aggregators: PosteriorAbilityEstimator,
     MeanAbilityEstimator, LikelihoodAbilityEstimator
 using FittedItemBanks
 using ComputerAdaptiveTesting.Responses: BooleanResponse
@@ -46,18 +46,18 @@ using FittedItemBanks.DummyData: dummy_full, std_normal, SimpleItemBankSpec, Std
 # CatRecorder collects information which can be used to draw different types of plots.
 max_questions = 99
 integrator = FixedGKIntegrator(-6, 6, 80)
-dist_ability_est = PriorAbilityEstimator(std_normal)
+dist_ability_est = PosteriorAbilityEstimator(std_normal)
 ability_estimator = MeanAbilityEstimator(dist_ability_est, integrator)
 rules = CatRules(ability_estimator,
-    AbilityVarianceStateCriterion(dist_ability_est, integrator),
-    FixedItemsTerminationCondition(max_questions))
+    AbilityVariance(dist_ability_est, integrator),
+    FixedLength(max_questions))
 
 points = 500
 xs = range(-2.5, 2.5, length = points)
 raw_estimator = LikelihoodAbilityEstimator()
 recorder = CatRecorder(xs, responses, integrator, raw_estimator, ability_estimator)
 for testee_idx in axes(responses, 2)
-    tracked_responses, θ = run_cat(CatLoopConfig(rules = rules,
+    tracked_responses, θ = run_cat(CatLoop(rules = rules,
             get_response = auto_responder(@view responses[:, testee_idx]),
             new_response_callback = (tracked_responses, terminating) -> recorder(tracked_responses,
                 testee_idx,

diff --git a/docs/examples/examples/ability_convergence_mirt.jl b/docs/examples/examples/ability_convergence_mirt.jl
@@ -21,8 +21,8 @@ using AlgebraOfGraphics
 using ComputerAdaptiveTesting
 using ComputerAdaptiveTesting.Sim: auto_responder
 using ComputerAdaptiveTesting.NextItemRules: DRuleItemCriterion
-using ComputerAdaptiveTesting.TerminationConditions: FixedItemsTerminationCondition
-using ComputerAdaptiveTesting.Aggregators: PriorAbilityEstimator,
+using ComputerAdaptiveTesting.TerminationConditions: FixedLength
+using ComputerAdaptiveTesting.Aggregators: PosteriorAbilityEstimator,
     MeanAbilityEstimator, LikelihoodAbilityEstimator
 using FittedItemBanks
 import PsychometricsBazaarBase.IntegralCoeffs
@@ -49,11 +49,11 @@ using ComputerAdaptiveTesting.Responses: BooleanResponse
 # CatRecorder collects information which can be used to draw different types of plots.
 max_questions = 9
 integrator = CubaIntegrator([-6.0, -6.0], [6.0, 6.0], CubaVegas(); rtol = 1e-2)
-ability_estimator = MeanAbilityEstimator(PriorAbilityEstimator(std_mv_normal(dims)),
+ability_estimator = MeanAbilityEstimator(PosteriorAbilityEstimator(std_mv_normal(dims)),
     integrator)
 rules = CatRules(ability_estimator,
     DRuleItemCriterion(ability_estimator),
-    FixedItemsTerminationCondition(max_questions))
+    FixedLength(max_questions))
 
 # XXX: We shouldn't need to specify xs here since the distributions are not used -- rework
 points = 3
@@ -67,7 +67,7 @@ recorder = CatRecorder(xs,
     abilities)
 for testee_idx in axes(responses, 2)
     @debug "Running for testee" testee_idx
-    tracked_responses, θ = run_cat(CatLoopConfig(rules = rules,
+    tracked_responses, θ = run_cat(CatLoop(rules = rules,
             get_response = auto_responder(@view responses[:, testee_idx]),
             new_response_callback = (tracked_responses, terminating) -> recorder(tracked_responses,
                 testee_idx,

diff --git a/docs/examples/examples/vocab_iq.jl b/docs/examples/examples/vocab_iq.jl
@@ -5,9 +5,9 @@
 # ---
 
 #md # Running a CAT based based on real response data
-# 
+#
 # This example shows how to run a CAT end-to-end on real data.
-# 
+#
 # First a 1-dimensional IRT model is fitted based on open response data to the
 # vocabulary IQ test using the IRTSupport package which internally, this uses
 # the `mirt` R package. Next, the model is used to administer the test
@@ -37,13 +37,13 @@ function run_vocab_iq_cat()
     item_bank, labels = get_item_bank()
     integrator = FixedGKIntegrator(-6, 6, 61)
     ability_integrator = AbilityIntegrator(integrator)
-    dist_ability_est = PriorAbilityEstimator(std_normal)
+    dist_ability_est = PosteriorAbilityEstimator(std_normal)
     optimizer = AbilityOptimizer(OneDimOptimOptimizer(-6.0, 6.0, NelderMead()))
     ability_estimator = ModeAbilityEstimator(dist_ability_est, optimizer)
     @info "run_cat" ability_estimator
     rules = CatRules(ability_estimator,
-        AbilityVarianceStateCriterion(dist_ability_est, ability_integrator),
-        FixedItemsTerminationCondition(45))
+        AbilityVariance(dist_ability_est, ability_integrator),
+        FixedLength(45))
     function get_response(response_idx, response_name)
         params = item_params(item_bank, response_idx)
         println("Parameters for next question: $params")
@@ -63,7 +63,7 @@ function run_vocab_iq_cat()
         println("Got ability estimate: $ability ± $var")
         println("")
     end
-    loop_config = CatLoopConfig(rules = rules,
+    loop_config = CatLoop(rules = rules,
         get_response = get_response,
         new_response_callback = new_response_callback)
     run_cat(loop_config, item_bank)

diff --git a/docs/src/api.md b/docs/src/api.md
@@ -8,5 +8,5 @@ CurrentModule = ComputerAdaptiveTesting
 ```
 
 ```@autodocs
-Modules = [ComputerAdaptiveTesting, ComputerAdaptiveTesting.Aggregators, ComputerAdaptiveTesting.Responses, ComputerAdaptiveTesting.Sim, ComputerAdaptiveTesting.TerminationConditions, ComputerAdaptiveTesting.NextItemRules, ComputerAdaptiveTesting.CatConfig]
+Modules = [ComputerAdaptiveTesting, ComputerAdaptiveTesting.Aggregators, ComputerAdaptiveTesting.Responses, ComputerAdaptiveTesting.Sim, ComputerAdaptiveTesting.TerminationConditions, ComputerAdaptiveTesting.NextItemRules, ComputerAdaptiveTesting.Rules]
 ```
diff --git a/docs/src/creating_a_cat.md b/docs/src/creating_a_cat.md
@@ -13,7 +13,7 @@ The configuration of a CAT is built up as a tree of configuration structs.
 These structs are all subtypes of `CatConfigBase`.
 
 ```@docs; canonical=false
-ComputerAdaptiveTesting.CatConfig.CatConfigBase
+ComputerAdaptiveTesting.ConfigBase.CatConfigBase
 ```
 
 The constructors for the configuration structs in this package tend to have
@@ -59,7 +59,7 @@ next item selection rule, and the stopping rule. `CatRules` has explicit and
 implicit constructors.
 
 ```@docs; canonical=false
-ComputerAdaptiveTesting.CatConfig.CatRules
+ComputerAdaptiveTesting.CatRules
 ```
 
 ### Next item selection with `NextItemRule`
@@ -79,13 +79,13 @@ ComputerAdaptiveTesting.NextItemRules.RandomNextItemRule
 
 Other rules are created by combining a `ItemCriterion` -- which somehow rates
 items according to how good they are -- with a `NextItemStrategy` using an
-`ItemStrategyNextItemRule`, which acts as an adapter. The default
+`ItemCriterionRule`, which acts as an adapter. The default
 `NextItemStrategy` (and currently only) is `ExhaustiveSearch`. When using
 the implicit constructors, `ItemCriterion` can therefore be used directly
 without wrapping in any place an NextItemRule is expected.
 
 ```@docs; canonical=false
-ComputerAdaptiveTesting.NextItemRules.ItemStrategyNextItemRule
+ComputerAdaptiveTesting.NextItemRules.ItemCriterionRule
 ```
 
 ```@docs; canonical=false
@@ -114,17 +114,17 @@ takes a `ResponseExpectation`: either `PointResponseExpectation` or
 good a particular state is in terms getting a good estimate of the test takers
 ability. They look one ply ahead to get the expected value of the
 ``StateCriterion`` after selecting the given item. The
-`AbilityVarianceStateCriterion` looks at the variance of the ability ``\theta``
+`AbilityVariance` looks at the variance of the ability ``\theta``
 estimate at that state.
 
 ### Stopping rules with `TerminationCondition`
 
-Currently the only `TerminationCondition` is `FixedItemsTerminationCondition`, which ends the test after a fixed number of items.
+Currently the only `TerminationCondition` is `FixedLength`, which ends the test after a fixed number of items.
 
 ```@docs; canonical=false
 ComputerAdaptiveTesting.TerminationConditions.TerminationCondition
 ```
 
 ```@docs; canonical=false
-ComputerAdaptiveTesting.TerminationConditions.FixedItemsTerminationCondition
+ComputerAdaptiveTesting.TerminationConditions.FixedLength
 ```
diff --git a/docs/src/stateful.md b/docs/src/stateful.md
@@ -28,9 +28,9 @@ Stateful.get_ability
 There is an implementation in terms of [CatRules](@ref):
 
 ```@docs
-Stateful.StatefulCatConfig
+Stateful.StatefulCatRules
 ```
 
 ## Usage
 
-Just as [CatLoopConfig](@ref) can wrap [CatRules](@ref), you can also use it with any implementor of [Stateful.StatefulCat](@ref), and run using [Sim.run_cat](@ref).
+Just as [CatLoop](@ref) can wrap [CatRules](@ref), you can also use it with any implementor of [Stateful.StatefulCat](@ref), and run using [Sim.run_cat](@ref).
diff --git a/docs/src/using_your_cat.md b/docs/src/using_your_cat.md
@@ -9,10 +9,10 @@ a number of ways you can use it.
 This section covers a few.
 See also the [Examples](@ref demo-page).
 
-When you've set up your CAT using [CatRules](@ref), you can wrap it in a [CatLoopConfig](@ref) and run it with [run_cat](@ref).
+When you've set up your CAT using [CatRules](@ref), you can wrap it in a [CatLoop](@ref) and run it with [run_cat](@ref).
 
 ```@docs; canonical=false
-CatLoopConfig
+CatLoop
 run_cat
 ```
 

diff --git a/ext/TestExt.jl b/ext/TestExt.jl
@@ -2,7 +2,7 @@ module TestExt
 
 using Test
 using ComputerAdaptiveTesting: Stateful
-using FittedItemBanks: AbstractItemBank, ItemResponse, resp
+using FittedItemBanks: AbstractItemBank, ItemResponse, resp_vec
 
 export test_stateful_cat_1d_dich_ib, test_stateful_cat_item_bank_1d_dich_ib
 
@@ -96,18 +96,38 @@ function test_stateful_cat_item_bank_1d_dich_ib(
     cat::Stateful.StatefulCat,
     item_bank::AbstractItemBank,
     points=[-.78, 0.0, .78],
-    margin=0.05,
+    margin=0.01,
 )
     if length(item_bank) != Stateful.item_bank_size(cat)
         error("Item bank length does not match the cat's item bank size.")
     end
     for i in 1:length(item_bank)
         for point in points
-            cat_prob = Stateful.item_response_function(cat, i, true, point)
-            ib_prob = resp(ItemResponse(item_bank, i), true, point)
+            cat_prob = Stateful.item_response_functions(cat, i, point)
+            ib_prob = resp_vec(ItemResponse(item_bank, i), point)
             @test cat_prob ≈ ib_prob  rtol=margin
         end
     end
 end
 
+function test_ability(
+    cat1::Stateful.StatefulCat,
+    cat2::Stateful.StatefulCat,
+    item_bank_length;
+    margin=0.01
+)
+    if item_bank_length < 4
+        error("Item bank length must be at least 4.")
+    end
+    for cat in (cat1, cat2)
+        Stateful.add_response!(cat, 1, false)
+        Stateful.add_response!(cat, 2, true)
+        Stateful.add_response!(cat, 3, false)
+        Stateful.add_response!(cat, 4, true)
+    end
+    ability1 = Stateful.get_ability(cat1)
+    ability2 = Stateful.get_ability(cat2)
+    @test ability1[1] ≈ ability2[1]  rtol=margin
+end
+
 end
diff --git a/profile/next_items.jl b/profile/next_items.jl
@@ -20,7 +20,7 @@ function get_ability_estimator(multidim)
         integrator = FixedGKIntegrator(-6.0, 6.0)
         dist = Normal()
     end
-    return PriorAbilityEstimator(dist, integrator)
+    return PosteriorAbilityEstimator(dist, integrator)
 end
 
 function prepare_empty(item_bank, actual_responses, ability_tracker)