Learn R Programming

⚠️There's a newer version (1.7.0) of this package.Take me there.

TestDesign (version 0.2.2)

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: 'Rsymphony', 'gurobi', 'lpSolve', and 'Rglpk'. The 'gurobi' package is not available from CRAN; see . See vignette for installing 'Rsymphony' package on Mac systems.

Copy Link

Version

Install

install.packages('TestDesign')

Monthly Downloads

470

Version

0.2.2

License

GPL (>= 2)

Maintainer

Seung W. Choi

Last Published

August 20th, 2019

Functions in TestDesign (0.2.2)

Calculate Fisher information at multiple thetas (3PL)

Perform shadow test assembly

Calculate Fisher information at multiple thetas (2PL)

Run computerized adaptive testing with generalized shadow-test approach

Calculate Fisher information at multiple thetas (GPC)

Calculate Fisher information at multiple thetas (GR)

Launch Shiny app

Calculate probability at multiple thetas (1PL)

Run Automated Test Assembly

Calculate Fisher information at multiple thetas (PC)

Add transparancy to color

Calculate the Fisher information using empirical Bayes

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 (PC)

buildConstraints

Build constraints

Calculate Fisher information at multiple thetas (1PL)

Calculate second derivative of log-likelihood

Calculate item response probabilities

Calculate item location

calc_likelihood

Calculate a likelihood value of theta

Calculate first derivative of log-likelihood

Calculate the mutual information using full Bayesian

Generate expected a posteriori estimates of theta

Find matching theta to supplied probability

calc_likelihood_function

Calculate a likelihood function of theta

Calculate probability at multiple thetas (GPC)

Calculate probability at multiple thetas (GR)

Generate maximum likelihood estimates of theta

Calculate Fisher information at a single theta (PC)

Calculate probability at a single theta (GR)

config_Shadow-class

createShadowTestConfig

Calculate probability at a single theta (GPC)

Calculate Fisher information at a single theta (GR).

Generate maximum likelihood estimates of theta

constraint-class

An S4 class to represent a set of constraints

Calculate the Fisher information using full Bayesian

Generate expected a posteriori estimates of theta

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

Simulate item responses

Calculate an empirical Bayes estimate of theta for one examinee

Create a subset of an item pool object

theta_EAP_matrix

Calculate EAP estimates of theta for a group of examinees

Calculate expected scores

Calculate Relative Errors

Calculate probability at multiple thetas (2PL)

Calculate Root Mean Squared Error

Calculate probability at multiple thetas (3PL)

loadConstraints

Load constraints

plotEligibilityStats

Draw item eligibility statistics plots

makeItemPoolCluster

Create an item pool cluster object

Load item attributes

Calculate hyperparameters for logit-normal distribution

Calculate first derivative

calc_posterior_single

Calculate a posterior value of theta for a single item

calc_log_likelihood

Calculate a log-likelihood value of theta

calcDerivative2

Calculate second derivative

calc_log_likelihood_function

Calculate a log-likelihood function of theta

Calculate Fisher information at a single theta (GPC).

dataset_fatigue

Fatigue dataset

config_ATA-class

createStaticTestConfig

Calculate Fisher information at a single theta (3PL)

iparPosteriorSample

Sample item parameter estimates from their posterior distributions

Calculate Fisher information

An S4 class to represent a partial credit item

An S4 class to represent a 1PL item

Calculate Fisher information at a single theta (1PL)

checkConstraints

Check the consistency of constraints and item usage

Calculate a posterior value of theta

dataset_reading

Reading dataset

calc_posterior_function

Calculate a posterior distribution of theta

dataset_science

Science dataset

Generate a test object

item_pool-class

An S4 class to represent an item pool

makeTestCluster

Generate a test cluster object

extract-methods

Draw an item exposure plot

An S4 class to represent a 2PL item

Calculate probability at a single theta (2PL)

test_cluster-class

An S4 class to represent a test cluster

Calculate probability at a single theta (3PL)

Calculate an EAP estimate of theta for one examinee

An S4 class to represent a 3PL item

Load item paramaters

Calculate hyperparameters for log-normal distribution

item_pool.operators

Item pool and pool cluster operators

Calculate Fisher information at a single theta (2PL)

Calculate probability at a single theta (1PL)

output_Shadow-class

plotInfoOverlay

Overlay item information plots

plotExposureRateFinalFlag

Draw item information plots for flagged items by segment

plotExposureRateBySegment

Draw exposure rate plots by theta segment

plotExposureRateFinal

Draw exposure rate plots by final theta segment

Draw a plot of maximum attainable information given the imposed constraints

Load set/stimulus/passage attributes

pool_cluster-class

An S4 class to represent a cluster of item pools

Draw an audit trail plot

Calculate probability at a single theta (PC)

theta_FB_single

Calculate a fully Bayesian estimate of theta for a single item

Save or print audit trails

An S4 class to represent a test

Create a subset of a test object

Draw item information plots

An S4 class to represent a generalized partial credit item

Find the segment to which each theta value belongs

Draw RMSE plots

An S4 class to represent a graded response item

Draw a shadow test chart

theta_EB_single

Calculate an empirical Bayes estimate of theta for a single item

Calculate a fully Bayesian estimate of theta for an examinee

updateConstraints

Update constraints