Add some mirtcat aliases

Author: Frankie Robertson

src/next_item_rules/aliases.jl

@@ -13,16 +13,78 @@ const catr_next_item_aliases = Dict(
         ExhaustiveSearch1Ply(parallel),
         ExpectationBasedItemCriterion(ability_estimator,
             AbilityVarianceStateCriterion(ability_estimator)))
+    #"MLWI",
+    #"MPWI",
+    #"MEI",
 )
 
 #"thOpt",
-#"MLWI",
-#"MPWI",
-#"MEI",
 #"progressive",
 #"proportional",
 #"KL",
 #"KLP",
 #"GDI",
 #"GDIP",
 #"random"
+
+const mirtcat_next_item_aliases = Dict(
+    # "MI' for the maximum information
+    "MI" => (ability_estimator) -> ItemStrategyNextItemRule(
+        ExhaustiveSearch1Ply(false),
+        InformationItemCriterion(ability_estimator)),
+    # 'MEPV' for minimum expected posterior variance
+    "MEPV" => (ability_estimator) -> ItemStrategyNextItemRule(
+        ExhaustiveSearch1Ply(false),
+        ExpectationBasedItemCriterion(ability_estimator,
+            AbilityVarianceStateCriterion(ability_estimator))),
+    # 'MLWI' for maximum likelihood weighted information
+    #"MLWI" =>
+    # 'MPWI' for maximum posterior weighted information
+    # 'MEI' for maximum expected information
+    # 'IKLP' as well as 'IKL' for the integration based Kullback-Leibler criteria with and without the prior density weight,
+    # respectively, and their root-n items administered weighted counter-parts, 'IKLn' and 'IKLPn'.
+#=
+Possible inputs for multidimensional adaptive tests include: 'Drule' for the
+maximum determinant of the information matrix, 'Trule' for the maximum
+(potentially weighted) trace of the information matrix, 'Arule' for the minimum (potentially weighted) trace of the asymptotic covariance matrix, 'Erule'
+for the minimum value of the information matrix, and 'Wrule' for the weighted
+information criteria. For each of these rules, the posterior weight for the latent trait scores can also be included with the 'DPrule', 'TPrule', 'APrule',
+'EPrule', and 'WPrule', respectively.
+Applicable to both unidimensional and multidimensional tests are the 'KL' and
+'KLn' for point-wise Kullback-Leibler divergence and point-wise KullbackLeibler with a decreasing delta value (delta*sqrt(n), where n is the number
+of items previous answered), respectively. The delta criteria is defined in the
+design object
+Non-adaptive methods applicable even when no mo object is passed are: 'random'
+to randomly select items, and 'seq' for selecting items sequentially
+=#
+)
+
+const mirtcat_ability_estimator_aliases = Dict(
+#=
+• "MAP" for the maximum a-posteriori (i.e, Bayes modal)
+• "ML" for maximum likelihood
+• "WLE" for weighted likelihood estimation
+• "EAPsum" for the expected a-posteriori for each sum score
+• "plausible" for a single plausible value imputation for each case. This is
+equivalent to setting plausible.draws = 1
+• "classify" for the posteriori classification probabilities (only applicable
+when the input model was of class MixtureClass)
+=#
+    # "EAP" for the expected a-posteriori (default).
+)
+
+function mirtcat_quadpts(nfact)
+    if nfact == 1
+        return 121
+    elseif nfact == 2
+        return 61
+    elseif nfact == 3
+        return 31
+    elseif nfact == 4
+        return 19
+    elseif nfact == 5
+        return 11
+    else
+        return 5
+    end
+end