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TestDesign (version 0.2.3)

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: '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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Install

install.packages('TestDesign')

Monthly Downloads

470

Version

0.2.3

License

GPL (>= 2)

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Repository

https://github.com/choi-phd/TestDesign

Maintainer

Seung W. Choi

Last Published

August 28th, 2019

Functions in TestDesign (0.2.3)

Calculate Relative Errors

Calculate probability at multiple thetas (1PL)

Calculate Fisher information at multiple thetas (PC)

Calculate expected scores

Calculate Fisher information

Calculate the Fisher information using full Bayesian

Calculate probability at multiple thetas (3PL)

Run Automated Test Assembly

Calculate probability at multiple thetas (2PL)

Launch Shiny app

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

Calculate second derivative of log-likelihood

Perform shadow test assembly

Calculate Fisher information at multiple thetas (1PL)

Add transparancy to color

Generate expected a posteriori estimates of theta

Calculate the Fisher information matrix for a single theta value and a set of items, potentially with a mixture of different models

Calculate the Fisher information using empirical Bayes

Generate expected a posteriori estimates of theta

Calculate Root Mean Squared Error

Calculate a posterior value of theta

Calculate first derivative of log-likelihood

Run computerized adaptive testing with generalized shadow-test approach

Calculate Fisher information at multiple thetas (GPC)

Find matching theta to supplied probability

Calculate Fisher information at multiple thetas (GR)

calc_log_likelihood_function

Calculate a log-likelihood function of theta

Calculate the mutual information using full Bayesian

calc_log_likelihood

Calculate a log-likelihood value of theta

calc_posterior_function

Calculate a posterior distribution of theta

dataset_reading

Reading dataset

constraint-class

An S4 class to represent a set of constraints

Calculate probability at multiple thetas (GPC)

Calculate item location

Calculate Fisher information at a single theta (GR).

An S4 class to represent a generalized partial credit item

Calculate item response probabilities

Calculate probability at multiple thetas (GR)

Calculate Fisher information at a single theta (PC)

Calculate Fisher information at multiple thetas (2PL)

Calculate Fisher information at a single theta (1PL)

dataset_science

Science dataset

config_Shadow-class

createShadowTestConfig

An S4 class to represent a 1PL item

plotExposureRateBySegment

Draw exposure rate plots by theta segment

output_Shadow-class

Calculate probability at a single theta (1PL)

Calculate hyperparameters for log-normal distribution

iparPosteriorSample

Sample item parameter estimates from their posterior distributions

plotExposureRateFinal

Draw exposure rate plots by final theta segment

item_pool.operators

Item pool and pool cluster operators

Calculate Fisher information at multiple thetas (3PL)

Calculate Fisher information at a single theta (2PL)

extract-methods

Calculate a fully Bayesian estimate of theta for an examinee

theta_EB_single

Calculate an empirical Bayes estimate of theta for a single item

Create a subset of an item pool object

Simulate item responses

Calculate probability at multiple thetas (PC)

Calculate first derivative

buildConstraints

Build constraints

calcDerivative2

Calculate second derivative

calc_likelihood

Calculate a likelihood value of theta

calc_likelihood_function

Calculate a likelihood function of theta

config_ATA-class

createStaticTestConfig

Calculate hyperparameters for logit-normal distribution

makeItemPoolCluster

Create an item pool cluster object

An S4 class to represent a graded response item

Draw a shadow test chart

Draw an item exposure plot

Generate maximum likelihood estimates of theta

plotEligibilityStats

Draw item eligibility statistics plots

Draw RMSE plots

Generate maximum likelihood estimates of theta

Calculate probability at a single theta (2PL)

calc_posterior_single

Calculate a posterior value of theta for a single item

checkConstraints

Check the consistency of constraints and item usage

test_cluster-class

An S4 class to represent a test cluster

Calculate Fisher information at a single theta (3PL)

item_pool-class

An S4 class to represent an item pool

Load item paramaters

Find the segment to which each theta value belongs

An S4 class to represent a partial credit item

Calculate an EAP estimate of theta for one examinee

Load item attributes

Generate a test object

loadConstraints

Load constraints

dataset_fatigue

Fatigue dataset

Load set/stimulus/passage attributes

pool_cluster-class

An S4 class to represent a cluster of item pools

makeTestCluster

Generate a test cluster object

Save or print audit trails

Calculate Fisher information at a single theta (GPC).

updateConstraints

Update constraints

theta_FB_single

Calculate a fully Bayesian estimate of theta for a single item

Calculate probability at a single theta (3PL)

Draw a plot of maximum attainable information given the imposed constraints

Calculate an empirical Bayes estimate of theta for one examinee

Calculate probability at a single theta (GPC)

plotInfoOverlay

Overlay item information plots

theta_EAP_matrix

Calculate EAP estimates of theta for a group of examinees

Calculate probability at a single theta (GR)

plotExposureRateFinalFlag

Draw item information plots for flagged items by segment

Draw item information plots

An S4 class to represent a 2PL item

An S4 class to represent a test

Create a subset of a test object

An S4 class to represent a 3PL item

Draw an audit trail plot

Calculate probability at a single theta (PC)