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TestDesign (version 1.0.1)

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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Install

install.packages('TestDesign')

Monthly Downloads

493

Version

1.0.1

License

GPL (>= 2)

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Repository

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

Maintainer

Seung W. Choi

Last Published

January 24th, 2020

Functions in TestDesign (1.0.1)

Calculate probability at multiple thetas (3PL)

Calculate first derivative

calc_likelihood

Calculate a likelihood value of theta

Calculate expected scores

buildConstraints

Build constraints

calcDerivative2

Calculate second derivative

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 probability at multiple thetas (GPC)

Calculate Fisher information at multiple thetas (GPC)

Calculate second derivative of log-likelihood

Calculate Fisher information

Calculate Fisher information at multiple thetas (GR)

Calculate Fisher information at multiple thetas (PC)

Calculate item location

Calculate probability at multiple thetas (PC)

Calculate the Fisher information using full Bayesian

Calculate probability at multiple thetas (GR)

dataset_reading

Reading dataset

Calculate first derivative of log-likelihood

Calculate the Fisher information using empirical Bayes

calc_log_likelihood_function

Calculate a log-likelihood function of theta

Print solution items

dataset_science

Science dataset

config_Static-class

createStaticTestConfig

Calculate Fisher information at a single theta (1PL)

Generate expected a posteriori estimates of theta

Generate expected a posteriori estimates of theta

Calculate a posterior value of theta

Calculate the mutual information using full Bayesian

Calculate Fisher information at a single theta (GPC).

calc_likelihood_function

Calculate a likelihood function 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 probability at multiple thetas (1PL)

dataset_fatigue

Fatigue dataset

Calculate probability at multiple thetas (2PL)

Calculate item response probabilities

Find matching theta to supplied probability

Calculate Fisher information at a single theta (GR).

calc_posterior_function

Calculate a posterior distribution of theta

Calculate Fisher information at a single theta (2PL)

item_pool.operators

Item pool and pool cluster operators

constraints-class

An S4 class to represent a set of constraints

item_attrib-class

An S4 class to represent a set of constraints.

extract-methods

item_pool-class

An S4 class to represent an item pool

Find the segment to which each theta value belongs

calc_posterior_single

Calculate a posterior value of theta for a single item

config_Shadow-class

createShadowTestConfig

An S4 class to represent a 3PL item

An S4 class to represent a generalized partial credit item

Calculate Fisher information at a single theta (3PL)

Calculate hyperparameters for log-normal distribution

calc_log_likelihood

Calculate a log-likelihood value of theta

An S4 class to represent a graded response item

Calculate Fisher information at a single theta (PC)

checkConstraints

Check the consistency of constraints and item usage

iparPosteriorSample

Sample item parameter estimates from their posterior distributions

constraint-class

An S4 class to represent a single constraint

Calculate hyperparameters for logit-normal distribution

Generate maximum likelihood estimates of theta

An S4 class to represent a partial credit item

makeTestCluster

Generate a test cluster object

Calculate probability at a single theta (PC)

Draw an audit trail plot

Generate a test object

Load item paramaters

Draw item information plots

Calculate an EAP estimate of theta for one examinee

test_cluster-class

An S4 class to represent a test cluster

plotExposureRateFinalFlag

Draw item information plots for flagged items by segment

Load set/stimulus/passage attributes

output_Shadow-class

Calculate probability at a single theta (1PL)

An S4 class to represent a 1PL item

plotInfoOverlay

Overlay item information plots

makeItemPoolCluster

Create an item pool cluster object

plotEligibilityStats

Draw item eligibility statistics plots

Run Test Assembly

Draw an item exposure plot

Create a subset of a test object

Save or print audit trails

An S4 class to represent a 2PL item

Load item attributes

loadConstraints

Load constraints

Calculate probability at a single theta (2PL)

Generate maximum likelihood estimates of theta

Draw RMSE plots

Calculate probability at a single theta (3PL)

Calculate probability at a single theta (GPC)

theta_EAP_matrix

Calculate EAP estimates of theta for a group of examinees

Calculate an empirical Bayes estimate of theta for one examinee

plotExposureRateFinal

Draw exposure rate plots by final theta segment

plotExposureRateBySegment

Draw exposure rate plots by theta segment

An S4 class to represent a test

Calculate probability at a single theta (GR)

st_attrib-class

An S4 class to represent a set of constraints.

Create a subset of an item pool object

theta_FB_single

Calculate a fully Bayesian estimate of theta for a single item

updateConstraints

Update constraints

showConstraints

Show constraints

Draw a shadow test chart

pool_cluster-class

An S4 class to represent a cluster of item pools

Simulate item responses

theta_EB_single

Calculate an empirical Bayes estimate of theta for a single item

Calculate a fully Bayesian estimate of theta for an examinee

Calculate Fisher information at multiple thetas (2PL)

Calculate Fisher information at multiple thetas (1PL)

Run Static Test Assembly

Calculate Root Mean Squared Error

Run adaptive test assembly.

Launch Shiny app

Calculate Relative Errors

Add transparancy to color

Calculate Fisher information at multiple thetas (3PL)