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TestDesign (version 1.1.2)

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

1.1.2

License

GPL (>= 2)

Maintainer

Seung W. Choi

Last Published

September 21st, 2020

Functions in TestDesign (1.1.2)

Calculate expected scores

buildConstraints

Build constraints (shortcut to other loading functions)

Calculate Root Mean Squared Error

calcLocation-methods

Calculate central location (overall difficulty)

Calculate Relative Errors

Calculate first derivative of log-likelihood

Calculate a posterior value of theta

Calculate second derivative of log-likelihood

Calculate Fisher information

calc_likelihood

Calculate likelihoods

Calculate the mutual information using full Bayesian

Run adaptive test assembly

Run fixed-form test assembly

constraints-class

Class 'constraints': a set of constraints

Open TestDesign app

item_attrib-class

Load item attributes

dataset_science

Science dataset

constraints-operators

Basic operators for constraints objects

dataset_fatigue

Fatigue dataset

dataset_reading

Reading dataset

config_Shadow-class

Create a config_Shadow object

Calculate Fisher information (multiple items)

Extension of plot() for objects in TestDesign package

calcProb-methods

Calculate item response probabilities

config_Static-class

Create a config_Static object

calcLogLikelihood

Calculate log-likelihood

Calculate Fisher information (single item)

(deprecated) Plot audit trail

iparPosteriorSample

Sample item parameter estimates from their posterior distributions

calc_posterior_function

Calculate a posterior distribution of theta

calc_posterior_single

Calculate a posterior value of theta for a single item

Compute expected a posteriori estimates of theta

Generate a test object

Classify theta into segments

makeTestCluster

Generate a test cluster object

Calculate hyperparameters for log-normal distribution

loadConstraints

Load constraints

output_Static-class

Class 'output_Static': fixed-form assembly solution

output_Shadow-class

Class 'output_Shadow': adaptive assembly solution for one simulee

output_Shadow_all-class

Class 'output_Shadow_all': a set of adaptive assembly solutions

showConstraints

(deprecated) Show constraints

Extension of show() for objects in TestDesign package

Calculate item response probability

Simulate item response data

Open TestDesign app

st_attrib-operators

Basic functions for stimulus attribute objects

theta_EAP_matrix

Calculate EAP estimates of theta for a group of examinees

Basic operators for test objects

Calculate an EAP estimate of theta for one examinee

Extension of print() for objects in TestDesign package

summary-classes

Summary classes

makeItemPoolCluster

Create an item pool cluster object

Extension of summary() for objects in TestDesign package

st_attrib-class

Load set/stimulus/passage attributes

Calculate hyperparameters for logit-normal distribution

(deprecated) Plot item exposure rates

item_attrib-operators

Basic functions for item attribute objects

Calculate theta estimates using EB (Empirical Bayes) method

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

(deprecated) Plot a shadow test chart

pool_cluster-class

An S4 class to represent a cluster of item pools

Class 'test': data for test assembly

test_cluster-class

Class 'test_cluster': data for test assembly

toggleConstraints

Toggle constraints

Calculate the Fisher information using empirical Bayes

checkConstraints

Check the consistency of constraints and item usage

Calculate the Fisher information using full Bayesian

constraint-class

Class 'constraint': a single constraint

Print solution items

getSolutionAttributes

Retrieve constraints-related attributes from solution

item_pool-class

Class 'item_pool': an item pool

updateConstraints

(deprecated) Update constraints

item_pool-operators

Basic operators for item pool objects

Compute maximum likelihood estimates of theta

Run Test Assembly

Compute maximum likelihood estimates of theta using fence items

Save or print audit trails

theta_FB_single

Calculate a fully Bayesian estimate of theta for a single item

Calculate a fully Bayesian estimate of theta for an examinee