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TestDesign (version 1.2.4)

Optimal Test Design Approach to Fixed and Adaptive Test Construction

Description

Use the optimal test design approach by Birnbaum (1968, ISBN:9781593119348) and van der Linden (2018) in constructing fixed and adaptive tests. Supports the following mixed-integer programming (MIP) solver packages: 'lpsymphony', 'Rsymphony', 'gurobi', 'lpSolve', and 'Rglpk'. The 'gurobi' package is not available from CRAN; see . See vignette for installing 'Rsymphony' package on Mac systems.

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Version

Install

install.packages('TestDesign')

Monthly Downloads

493

Version

1.2.4

License

GPL (>= 2)

Maintainer

Seung W. Choi

Last Published

June 5th, 2021

Functions in TestDesign (1.2.4)

Open TestDesign app

Run fixed-form test assembly

Calculate expected scores

Calculate Root Mean Squared Error

Run adaptive test assembly

Calculate Relative Errors

Calculate Fisher information

buildConstraints

Build constraints (shortcut to other loading functions)

Open TestDesign app

calcLogLikelihood

Calculate log-likelihood

calcProb-methods

Calculate item response probabilities

Calculate first derivative of log-likelihood

Print solution items

constraint-class

Class 'constraint': a single constraint

getSolutionAttributes

Retrieve constraints-related attributes from solution

calcLocation-methods

Calculate central location (overall difficulty)

checkConstraints

Check the consistency of constraints and item usage

Calculate the Fisher information using full Bayesian

Calculate the Fisher information using empirical Bayes

config_Shadow-class

Create a config_Shadow object

item_pool-operators

Basic operators for item pool objects

item_pool-class

Class 'item_pool': an item pool

config_Static-class

Create a config_Static object

output_Static-class

Class 'output_Static': fixed-form assembly solution

output_Shadow-class

Class 'output_Shadow': adaptive assembly solution for one simulee

dataset_fatigue

Fatigue dataset

Calculate the mutual information using full Bayesian

Calculate Fisher information (multiple items)

item_attrib-class

Load item attributes

constraints-class

Class 'constraints': a set of constraints

Compute expected a posteriori estimates of theta

Calculate second derivative of log-likelihood

calc_likelihood

Calculate likelihoods

Calculate item response probability

(deprecated) Plot item/test/pool-level information

Classify theta into segments

Calculate a posterior value of theta

constraints-operators

Basic operators for constraints objects

st_attrib-operators

Basic functions for stimulus attribute objects

Calculate theta estimates using EB (Empirical Bayes) method

dataset_reading

Reading dataset

Extension of print() for objects in TestDesign package

dataset_science

Science dataset

theta_FB_single

Calculate a fully Bayesian estimate of theta for a single item

Calculate a fully Bayesian estimate of theta for an examinee

st_attrib-class

Load set/stimulus/passage attributes

Extension of summary() for objects in TestDesign package

calc_posterior_function

Calculate a posterior distribution of theta

Calculate hyperparameters for log-normal distribution

Calculate Fisher information (single item)

iparPosteriorSample

Sample item parameter estimates from their posterior distributions

Generate a test object

Extension of plot() for objects in TestDesign package

(deprecated) Plot item exposure rates

Calculate hyperparameters for logit-normal distribution

output_Shadow_all-class

Class 'output_Shadow_all': a set of adaptive assembly solutions

Save or print audit trails

Calculate an EAP estimate of theta for one examinee

Basic operators for test objects

Run Test Assembly

item_attrib-operators

Basic functions for item attribute objects

calc_posterior_single

Calculate a posterior value of theta for a single item

test_cluster-class

Class 'test_cluster': data for test assembly

Class 'test': data for test assembly

makeItemPoolCluster

Create an item pool cluster object

item_pool_cluster-class

Class 'item_pool_cluster': an item pool

Simulate item response data

Compute maximum likelihood estimates of theta using fence items

toggleConstraints

Toggle constraints

Compute maximum likelihood estimates of theta

loadConstraints

Load constraints

Extension of show() for objects in TestDesign package

makeTestCluster

Generate a test cluster object

summary-classes

Summary classes

theta_EAP_matrix

Calculate EAP estimates of theta for a group of examinees