Recorded cat porc

.gitignore

@@ -14,3 +14,4 @@ attic/
 /full/Project.toml
 .CondaPkg/
 LocalPreferences.toml
+/.claude/settings.local.json

Project.toml

@@ -7,6 +7,7 @@ version = "0.4.0"
 Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
 AutoHashEquals = "15f4f7f2-30c1-5605-9d31-71845cf9641f"
 ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
+DataAPI = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
@@ -23,8 +24,11 @@ LogarithmicNumbers = "aa2f6b4e-9042-5d33-9679-40d3a6b85899"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 Mmap = "a63ad114-7e13-5084-954f-fe012c677804"
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
+PrettyPrinting = "54e16d92-306c-5ea0-a30b-337be88ac337"
+PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
 PsychometricsBazaarBase = "b0d9cada-d963-45e9-a4c6-4746243987f1"
 QuickHeaps = "30b38841-0f52-47f8-a5f8-18d5d4064379"
+RDataGet = "a115732e-4334-4ecb-8ea3-f683e7f66d4d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
@@ -44,13 +48,14 @@ Accessors = "^0.1.12"
 Aqua = "0.8"
 AutoHashEquals = "2"
 ConstructionBase = "^1.2"
+DataAPI = "1.16.0"
 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 = "^0.7.2"
+FittedItemBanks = "^0.7.3"
 ForwardDiff = "1"
 HypothesisTests = "^0.10.12, ^0.11.0"
 Interpolations = "^0.14, ^0.15"
@@ -62,8 +67,11 @@ MacroTools = "^0.5.6"
 Mmap = "^1.11"
 Optim = "1.7.3"
 PrecompileTools = "1.2.1"
-PsychometricsBazaarBase = "^0.8.4"
+PrettyPrinting = "0.4.2"
+PrettyTables = "3"
+PsychometricsBazaarBase = "^0.8.6"
 QuickHeaps = "0.2.2"
+RDataGet = "0.1.0"
 Random = "^1.11"
 Reexport = "1"
 Setfield = "^1"

src/Aggregators/Aggregators.jl

@@ -20,6 +20,7 @@ using FittedItemBanks: AbstractItemBank, ContinuousDomain,
 using ..Responses
 using ..Responses: concrete_response_type, function_xs, function_ys, Responses
 using ..ConfigBase
+import PsychometricsBazaarBase: power_summary
 using PsychometricsBazaarBase.ConfigTools: @requiresome, @returnsome,
                                            find1_instance, find1_type,
                                            find1_type_sloppy
@@ -52,6 +53,7 @@ export FunctionOptimizer, FunctionIntegrator
 export DistributionAbilityEstimator
 export variance, variance_given_mean, mean_1d
 export RiemannEnumerationIntegrator
+export get_integrator
 # export EnumerationOptimizer
 
 # Basic types
@@ -200,6 +202,10 @@ struct FunctionIntegrator{IntegratorT <: Integrator} <: AbilityIntegrator
     integrator::IntegratorT
 end
 
+function get_integrator(integrator::FunctionIntegrator)
+    return integrator.integrator
+end
+
 function (integrator::FunctionIntegrator{IntegratorT})(f::F,
         ncomp,
         lh_function::LHF) where {F, LHF, IntegratorT}
@@ -210,8 +216,8 @@ function (integrator::FunctionIntegrator{IntegratorT})(f::F,
     integrator.integrator(FunctionProduct(f, lh_function), ncomp)
 end
 
-function show(io::IO, ::MIME"text/plain", responses::FunctionIntegrator)
-    show(io, MIME("text/plain"), responses.integrator)
+function power_summary(io::IO, responses::FunctionIntegrator)
+    power_summary(io, responses.integrator)
 end
 
 # Defaults

src/Aggregators/ability_estimator.jl

@@ -26,7 +26,7 @@ function pdf(::LikelihoodAbilityEstimator,
     AbilityLikelihood(tracked_responses)
 end
 
-function show(io::IO, ::MIME"text/plain", ability_estimator::LikelihoodAbilityEstimator)
+function power_summary(io::IO, ability_estimator::LikelihoodAbilityEstimator)
     println(io, "Ability likelihood distribution")
 end
 
@@ -61,11 +61,11 @@ function multiple_response_types_guard(tracked_responses)
     return false
 end
 
-function show(io::IO, ::MIME"text/plain", ability_estimator::PosteriorAbilityEstimator)
+function power_summary(io::IO, ability_estimator::PosteriorAbilityEstimator)
     println(io, "Ability posterior distribution")
     indent_io = indent(io, 2)
     print(indent_io, "Prior: ")
-    show(indent_io, MIME("text/plain"), ability_estimator.prior)
+    power_summary(indent_io, ability_estimator.prior)
     println(io)
 end
 
@@ -224,11 +224,11 @@ function ModeAbilityEstimator(bits...)
     ModeAbilityEstimator(dist_est, optimizer)
 end
 
-function show(io::IO, ::MIME"text/plain", ability_estimator::ModeAbilityEstimator)
+function power_summary(io::IO, ability_estimator::ModeAbilityEstimator)
     println(io, "Estimate ability using its mode")
     indent_io = indent(io, 2)
-    show(indent_io, MIME("text/plain"), ability_estimator.dist_est)
-    show(indent_io, MIME("text/plain"), ability_estimator.optim)
+    power_summary(indent_io, ability_estimator.dist_est)
+    power_summary(indent_io, ability_estimator.optim)
 end
 
 struct MeanAbilityEstimator{
@@ -246,12 +246,12 @@ function MeanAbilityEstimator(bits...)
     MeanAbilityEstimator(dist_est, integrator)
 end
 
-function show(io::IO, ::MIME"text/plain", ability_estimator::MeanAbilityEstimator)
+function power_summary(io::IO, ability_estimator::MeanAbilityEstimator)
     println(io, "Estimate ability using its mean")
     indent_io = indent(io, 2)
-    show(indent_io, MIME("text/plain"), ability_estimator.dist_est)
+    power_summary(indent_io, ability_estimator.dist_est)
     print(indent_io, "Integrator: ")
-    show(indent_io, MIME("text/plain"), ability_estimator.integrator)
+    power_summary(indent_io, ability_estimator.integrator)
 end
 
 function distribution_estimator(dist_est::DistributionAbilityEstimator)::DistributionAbilityEstimator

src/Aggregators/optimizers.jl

@@ -10,7 +10,7 @@ function (optim::FunctionOptimizer)(f::F,
     optim.optim(comp_f)
 end
 
-function show(io::IO, ::MIME"text/plain", optim::FunctionOptimizer)
+function power_summary(io::IO, optim::FunctionOptimizer)
     indent_io = indent(io, 2)
     if optim.optim isa Optimizers.OneDimOptimOptimizer || optim.optim isa Optimizers.MultiDimOptimOptimizer || optim.optim isa Optimizers.NativeOneDimOptimOptimizer
         inner = optim.optim

src/Aggregators/tracked.jl

@@ -20,6 +20,10 @@ struct TrackedLikelihoodIntegrator{IntegratorT <: Integrator} <: AbilityIntegrat
     tracker::GriddedAbilityTracker
 end
 
+function get_integrator(integrator::TrackedLikelihoodIntegrator)
+    return integrator.integrator
+end
+
 function (integrator::TrackedLikelihoodIntegrator{IntegratorT})(f::F,
         ncomp) where {F, IntegratorT}
     integrator.integrator(FunctionArgProduct(f), integrator.tracker.cur_ability, ncomp)

src/ConfigBase.jl

@@ -10,6 +10,8 @@ $(TYPEDEF)
 """
 abstract type CatConfigBase end
 
+show(io::IO, ::MIME"text/plain", obj::CatConfigBase) = power_summary(io, obj)
+
 function walk(f, x::CatConfigBase, lens = identity)
     f(x, lens)
     for fieldname in fieldnames(typeof(x))

src/NextItemRules/NextItemRules.jl

@@ -17,6 +17,7 @@ using Random: AbstractRNG, Xoshiro
 
 using ..Responses: BareResponses
 using ..ConfigBase
+import PsychometricsBazaarBase: power_summary
 using PsychometricsBazaarBase.ConfigTools: @requiresome, @returnsome,
                                            find1_instance, find1_type
 using PsychometricsBazaarBase.Integrators: Integrator, intval
@@ -56,7 +57,7 @@ export PointResponseExpectation, DistributionResponseExpectation
 export MatrixScalarizer, DeterminantScalarizer, TraceScalarizer
 export AbilityCovarianceStateMultiCriterion, StateMultiCriterion, ItemMultiCriterion
 export InformationMatrixCriteria
-export ScalarizedStateCriteron, ScalarizedItemCriteron
+export ScalarizedStateCriterion, ScalarizedItemCriterion
 export DRuleItemCriterion, TRuleItemCriterion
 
 # Prelude

src/NextItemRules/combinators/expectation.jl

@@ -39,10 +39,10 @@ function Aggregators.response_expectation(
         item_idx)
 end
 
-function show(io::IO, ::MIME"text/plain", point_response_expectation::PointResponseExpectation)
+function power_summary(io::IO, point_response_expectation::PointResponseExpectation)
     println(io, "Expected response at point ability estimate")
     indent_io = indent(io, 2)
-    show(indent_io, MIME("text/plain"), point_response_expectation.ability_estimator)
+    power_summary(indent_io, point_response_expectation.ability_estimator)
 end
 
 struct DistributionResponseExpectation{
@@ -131,9 +131,9 @@ function compute_criterion(
     res
 end
 
-function show(io::IO, ::MIME"text/plain", item_criterion::ExpectationBasedItemCriterion)
+function power_summary(io::IO, item_criterion::ExpectationBasedItemCriterion)
     println(io, "Optimize an state/item/item-category criterion based on an expected response")
     indent_io = indent(io, 2)
-    show(indent_io, MIME"text/plain"(), item_criterion.response_expectation)
-    show(indent_io, MIME"text/plain"(), item_criterion.criterion)
+    power_summary(indent_io, item_criterion.response_expectation)
+    power_summary(indent_io, item_criterion.criterion)
 end

src/NextItemRules/combinators/scalarizers.jl

@@ -4,23 +4,23 @@ scalarize(::DeterminantScalarizer, mat) = det(mat)
 struct TraceScalarizer <: MatrixScalarizer end
 scalarize(::TraceScalarizer, mat) = tr(mat)
 
-struct ScalarizedItemCriteron{
+struct ScalarizedItemCriterion{
     ItemMultiCriterionT <: ItemMultiCriterion,
     MatrixScalarizerT <: MatrixScalarizer
 } <: ItemCriterion
     criteria::ItemMultiCriterionT
     scalarizer::MatrixScalarizerT
 end
 
-struct ScalarizedStateCriteron{
+struct ScalarizedStateCriterion{
     StateMultiCriterionT <: StateMultiCriterion,
     MatrixScalarizerT <: MatrixScalarizer
 } <: StateCriterion
     criteria::StateMultiCriterionT
     scalarizer::MatrixScalarizerT
 end
 
-function compute_criterion(ssc::Union{ScalarizedItemCriteron, ScalarizedStateCriteron},
+function compute_criterion(ssc::Union{ScalarizedItemCriterion, ScalarizedStateCriterion},
         tracked_responses::TrackedResponses, item_idx...)
     res = scalarize(
         ssc.scalarizer,

src/NextItemRules/criteria/pointwise/information.jl

@@ -22,7 +22,7 @@ function compute_criterion_vec(
     -actual
 end
 
-function show(io::IO, ::MIME"text/plain", ::ObservedInformationPointwiseItemCategoryCriterion)
+function power_summary(io::IO, ::ObservedInformationPointwiseItemCategoryCriterion)
     println(io, "Observed pointwise item-category information")
 end
 
@@ -51,7 +51,7 @@ function compute_criterion_vec(
 end
 
 
-function show(io::IO, ::MIME"text/plain", ::RawEmpiricalInformationPointwiseItemCategoryCriterion)
+function power_summary(io::IO, ::RawEmpiricalInformationPointwiseItemCategoryCriterion)
     println(io, "Raw empirical pointwise item-category information")
 end
 
@@ -104,7 +104,7 @@ function compute_criterion_vec(
     -actual
 end
 
-function show(io::IO, ::MIME"text/plain", ::EmpiricalInformationPointwiseItemCategoryCriterion)
+function power_summary(io::IO, ::EmpiricalInformationPointwiseItemCategoryCriterion)
     println(io, "Empirical pointwise item-category information")
 end
 
@@ -131,7 +131,7 @@ function compute_criterion(
     sum(compute_criterion_vec(tii.pcic, ir, ability))
 end
 
-function show(io::IO, ::MIME"text/plain", rule::TotalItemInformation)
+function power_summary(io::IO, rule::TotalItemInformation)
     if rule.pcic isa ObservedInformationPointwiseItemCategoryCriterion
         println(io, "Observed pointwise item information")
     elseif rule.pcic isa RawEmpiricalInformationPointwiseItemCategoryCriterion
@@ -140,6 +140,6 @@ function show(io::IO, ::MIME"text/plain", rule::TotalItemInformation)
         println(io, "Empirical pointwise item information")
     else
         print(io, "Total ")
-        show(io, MIME("text/plain"), rule.pcic)
+        power_summary(io, rule.pcic)
     end
-end
+end

src/NextItemRules/criteria/state/ability_variance.jl

@@ -68,11 +68,13 @@ function compute_criterion(
         denom)
 end
 
-function show(io::IO, ::MIME"text/plain", criterion::AbilityVariance)
-    println(io, "Minimise variance of ability estimate")
+function power_summary(io::IO, criterion::AbilityVariance; skip_first_line=false)
+    if !skip_first_line
+        println(io, "Minimise variance of ability estimate")
+    end
     indent_io = indent(io, 2)
-    show(indent_io, MIME("text/plain"), criterion.dist_est)
-    show(indent_io, MIME("text/plain"), criterion.integrator)
+    power_summary(indent_io, criterion.dist_est)
+    power_summary(indent_io, criterion.integrator)
 end
 
 struct AbilityCovarianceStateMultiCriterion{

src/NextItemRules/porcelain/porcelain.jl

@@ -1,11 +1,11 @@
 function DRuleItemCriterion(ability_estimator)
-    ScalarizedItemCriteron(
+    ScalarizedItemCriterion(
         InformationMatrixCriteria(ability_estimator),
         DeterminantScalarizer())
 end
 
 function TRuleItemCriterion(ability_estimator)
-    ScalarizedItemCriteron(
+    ScalarizedItemCriterion(
         InformationMatrixCriteria(ability_estimator),
         TraceScalarizer())
 end

src/NextItemRules/prelude/next_item_rule.jl

@@ -53,11 +53,11 @@ function best_item(rule::NextItemRule, tracked_responses::TrackedResponses)
     best_item(rule, tracked_responses, tracked_responses.item_bank)
 end
 
-function Base.show(io::IO, ::MIME"text/plain", rule::ItemCriterionRule)
+function power_summary(io::IO, rule::ItemCriterionRule)
     println(io, "Pick optimal item criterion according to strategy")
     indent_io = indent(io, 2)
-    show(indent_io, MIME"text/plain"(), rule.strategy)
-    show(indent_io, MIME"text/plain"(), rule.criterion)
+    power_summary(indent_io, rule.strategy)
+    power_summary(indent_io, rule.criterion)
 end
 
 # Default implementation

src/NextItemRules/strategies/balance.jl

@@ -39,11 +39,11 @@ function GreedyForcedContentBalancer(targets::AbstractVector, groups, bits...)
     GreedyForcedContentBalancer(targets, groups, NextItemRule(bits...))
 end
 
-function show(io::IO, ::MIME"text/plain", rule::GreedyForcedContentBalancer)
+function power_summary(io::IO, rule::GreedyForcedContentBalancer)
     indent_io = indent(io, 2)
     println(io, "Greedy + forced content balancing")
     println(indent_io, "Target ratio: " * join(rule.targets, ", "))
-    show(indent_io, MIME("text/plain"), rule.inner_rule)
+    power_summary(indent_io, rule.inner_rule)
 end
 
 function next_item_bank(targets, groups, responses, items)
@@ -86,4 +86,4 @@ function compute_criteria(
     expanded = fill(Inf, length(items))
     expanded[matching_indicator] .= criteria
     return expanded
-end
+end

src/NextItemRules/strategies/pointwise.jl

@@ -16,12 +16,12 @@ function best_item(rule::PointwiseNextItemRule, responses::TrackedResponses, ite
     return idx
 end
 
-function show(io::IO, ::MIME"text/plain", rule::PointwiseNextItemRule)
+function power_summary(io::IO, rule::PointwiseNextItemRule)
     println(io, "Optimize a pointwise criterion at specified points")
     indent_io = indent(io, 2)
     points_desc = join(rule.points, ", ")
     println(indent_io, "Points: $points_desc")
-    show(indent_io, MIME("text/plain"), rule.criterion)
+    power_summary(indent_io, rule.criterion)
 end
 
 

src/NextItemRules/strategies/randomesque.jl

@@ -53,6 +53,6 @@ function best_item(
     randomesque(rule.strategy.rng, rule.criterion, responses, items, rule.strategy.k)[1]
 end
 
-function show(io::IO, ::MIME"text/plain", rule::RandomesqueStrategy)
+function power_summary(io::IO, rule::RandomesqueStrategy)
     println(io, "Randomesque strategy with k = $(rule.k)")
-end
+end