Optimal Test Design Approach to Fixed and Adaptive Test Construction


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Documentation for package ‘TestDesign’ version 1.0.2

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A B C D E F G I L M O P R S T U misc

-- A --

addTrans Add transparancy to color
app Launch Shiny app
array_info_1pl Calculate Fisher information at multiple thetas (1PL)
array_info_2pl Calculate Fisher information at multiple thetas (2PL)
array_info_3pl Calculate Fisher information at multiple thetas (3PL)
array_info_gpc Calculate Fisher information at multiple thetas (GPC)
array_info_gr Calculate Fisher information at multiple thetas (GR)
array_info_pc Calculate Fisher information at multiple thetas (PC)
array_p_1pl Calculate probability at multiple thetas (1PL)
array_p_2pl Calculate probability at multiple thetas (2PL)
array_p_3pl Calculate probability at multiple thetas (3PL)
array_p_gpc Calculate probability at multiple thetas (GPC)
array_p_gr Calculate probability at multiple thetas (GR)
array_p_pc Calculate probability at multiple thetas (PC)

-- B --

buildConstraints Build constraints

-- C --

calcDerivative Calculate first derivative
calcDerivative-method Calculate first derivative
calcDerivative2 Calculate second derivative
calcDerivative2-method Calculate second derivative
calcEscore Calculate expected scores
calcEscore-method Calculate expected scores
calcFisher Calculate Fisher information
calcFisher-method Calculate Fisher information
calcHessian Calculate second derivative of log-likelihood
calcHessian-method Calculate second derivative of log-likelihood
calcJacobian Calculate first derivative of log-likelihood
calcJacobian-method Calculate first derivative of log-likelihood
calcLocation Calculate item location
calcLocation-method Calculate item location
calcProb Calculate item response probabilities
calcProb-method Calculate item response probabilities
calcRP Find matching theta to supplied probability
calc_info Calculate the Fisher information matrix for a single theta value and a set of items, potentially with a mixture of different models
calc_info_EB Calculate the Fisher information using empirical Bayes
calc_info_FB Calculate the Fisher information using full Bayesian
calc_info_matrix Calculate the Fisher information matrix for a vector of theta values and a set of items, potentially with a mixture of different models
calc_likelihood Calculate a likelihood value of theta
calc_likelihood_function Calculate a likelihood function of theta
calc_log_likelihood Calculate a log-likelihood value of theta
calc_log_likelihood_function Calculate a log-likelihood function of theta
calc_MI_FB Calculate the mutual information using full Bayesian
calc_posterior Calculate a posterior value of theta
calc_posterior_function Calculate a posterior distribution of theta
calc_posterior_single Calculate a posterior value of theta for a single item
checkConstraints Check the consistency of constraints and item usage
config_Shadow-class createShadowTestConfig
config_Static-class createStaticTestConfig
constraint-class An S4 class to represent a single constraint
constraints-class An S4 class to represent a set of constraints
constraints_fatigue Fatigue dataset
constraints_fatigue_raw Fatigue dataset
constraints_reading Reading dataset
constraints_reading_raw Reading dataset
constraints_science Science dataset
constraints_science_raw Science dataset
createShadowTestConfig createShadowTestConfig
createStaticTestConfig createStaticTestConfig

-- D --

dataset_fatigue Fatigue dataset
dataset_reading Reading dataset
dataset_science Science dataset

-- E --

EAP Generate expected a posteriori estimates of theta
eap Generate expected a posteriori estimates of theta
EAP-method Generate expected a posteriori estimates of theta
eap-method Generate expected a posteriori estimates of theta
extract-methods Extract

-- F --

find_segment Find the segment to which each theta value belongs

-- G --

getSolution Print solution items
getSolution-method Print solution items

-- I --

info_1pl Calculate Fisher information at a single theta (1PL)
info_2pl Calculate Fisher information at a single theta (2PL)
info_3pl Calculate Fisher information at a single theta (3PL)
info_gpc Calculate Fisher information at a single theta (GPC).
info_gr Calculate Fisher information at a single theta (GR).
info_pc Calculate Fisher information at a single theta (PC)
iparPosteriorSample Sample item parameter estimates from their posterior distributions
itemattrib_fatigue Fatigue dataset
itemattrib_fatigue_raw Fatigue dataset
itemattrib_reading Reading dataset
itemattrib_reading_raw Reading dataset
itemattrib_science Science dataset
itemattrib_science_raw Science dataset
itemcontent_fatigue_raw Fatigue dataset
itempool_fatigue Fatigue dataset
itempool_fatigue_raw Fatigue dataset
itempool_reading Reading dataset
itempool_reading_raw Reading dataset
itempool_science Science dataset
itempool_science_raw Science dataset
item_1PL-class An S4 class to represent a 1PL item
item_2PL-class An S4 class to represent a 2PL item
item_3PL-class An S4 class to represent a 3PL item
item_attrib-class An S4 class to represent a set of constraints.
item_GPC-class An S4 class to represent a generalized partial credit item
item_GR-class An S4 class to represent a graded response item
item_PC-class An S4 class to represent a partial credit item
item_pool-class An S4 class to represent an item pool
item_pool.operators Item pool and pool cluster operators

-- L --

lnHyperPars Calculate hyperparameters for log-normal distribution
loadConstraints Load constraints
loadItemAttrib Load item attributes
loadItemPool Load item paramaters
loadStAttrib Load set/stimulus/passage attributes
logitHyperPars Calculate hyperparameters for logit-normal distribution

-- M --

makeItemPoolCluster Create an item pool cluster object
makeTest Generate a test object
makeTest-method Generate a test object
makeTestCluster Generate a test cluster object
makeTestCluster-method Generate a test cluster object
MLE Generate maximum likelihood estimates of theta
mle Generate maximum likelihood estimates of theta
MLE-method Generate maximum likelihood estimates of theta
mle-method Generate maximum likelihood estimates of theta

-- O --

OAT Launch Shiny app
output_Shadow-class output_Shadow

-- P --

plotCAT Draw an audit trail plot
plotCAT-method Draw an audit trail plot
plotEligibilityStats Draw item eligibility statistics plots
plotExposure Draw an item exposure plot
plotExposure-method Draw an item exposure plot
plotExposureRateBySegment Draw exposure rate plots by theta segment
plotExposureRateFinal Draw exposure rate plots by final theta segment
plotExposureRateFinalFlag Draw item information plots for flagged items by segment
plotInfo Draw item information plots
plotInfo-method Draw item information plots
plotInfoOverlay Overlay item information plots
plotRMSE Draw RMSE plots
plotShadow Draw a shadow test chart
plotShadow-method Draw a shadow test chart
pool_cluster-class An S4 class to represent a cluster of item pools
p_1pl Calculate probability at a single theta (1PL)
p_2pl Calculate probability at a single theta (2PL)
p_3pl Calculate probability at a single theta (3PL)
p_gpc Calculate probability at a single theta (GPC)
p_gr Calculate probability at a single theta (GR)
p_pc Calculate probability at a single theta (PC)

-- R --

RE Calculate Relative Errors
resp_fatigue_raw Fatigue dataset
RMSE Calculate Root Mean Squared Error
runAssembly Run Test Assembly

-- S --

saveOutput Save or print audit trails
Shadow Run adaptive test assembly.
Shadow-method Run adaptive test assembly.
showConstraints Show constraints
simResp Simulate item responses
simResp-method Simulate item responses
Static Run Static Test Assembly
Static-method Run Static Test Assembly
stimattrib_reading Reading dataset
stimattrib_reading_raw Reading dataset
st_attrib-class An S4 class to represent a set of constraints.
subsetItemPool Create a subset of an item pool object
subsetTest Create a subset of a test object

-- T --

test-class An S4 class to represent a test
test_cluster-class An S4 class to represent a test cluster
theta_EAP Calculate an EAP estimate of theta for one examinee
theta_EAP_matrix Calculate EAP estimates of theta for a group of examinees
theta_EB Calculate an empirical Bayes estimate of theta for one examinee
theta_EB_single Calculate an empirical Bayes estimate of theta for a single item
theta_FB Calculate a fully Bayesian estimate of theta for an examinee
theta_FB_single Calculate a fully Bayesian estimate of theta for a single item

-- U --

updateConstraints Update constraints

-- misc --

+.item_pool Item pool and pool cluster operators
-.item_pool Item pool and pool cluster operators
==.item_pool Item pool and pool cluster operators
==.pool_cluster Item pool and pool cluster operators
[-method Extract