Learn R Programming

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

TestDesign (version 0.2.5)

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

0.2.5

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

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

Maintainer

Seung W. Choi

Last Published

September 12th, 2019

Functions in TestDesign (0.2.5)

Calculate item location

Calculate first derivative

Calculate Fisher information at multiple thetas (PC)

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

config_ATA-class

createStaticTestConfig

Calculate the Fisher information using empirical Bayes

Run computerized adaptive testing with generalized shadow-test approach

Calculate probability at multiple thetas (1PL)

calcDerivative2

Calculate second derivative

Calculate probability at multiple thetas (GPC)

Calculate probability at multiple thetas (PC)

Calculate Fisher information at multiple thetas (GPC)

buildConstraints

Build constraints

calc_log_likelihood

Calculate a log-likelihood value of theta

calc_log_likelihood_function

Calculate a log-likelihood function of theta

Calculate Fisher information at a single theta (GR).

Calculate Fisher information at a single theta (PC)

dataset_fatigue

Fatigue dataset

Calculate probability at multiple thetas (3PL)

plotExposureRateBySegment

Draw exposure rate plots by theta segment

Calculate probability at multiple thetas (2PL)

Calculate probability at multiple thetas (GR)

Generate expected a posteriori estimates of theta

Calculate Fisher information at a single theta (3PL)

Generate expected a posteriori estimates of theta

Calculate Fisher information at a single theta (GPC).

An S4 class to represent a generalized partial credit item

An S4 class to represent a graded response item

Generate maximum likelihood estimates of theta

Generate maximum likelihood estimates of theta

Calculate expected scores

Calculate Fisher information

Calculate second derivative of log-likelihood

Draw an item exposure plot

plotEligibilityStats

Draw item eligibility statistics plots

Calculate a posterior value of theta

calc_posterior_function

Calculate a posterior distribution of theta

calc_likelihood

Calculate a likelihood value of theta

item_pool.operators

Item pool and pool cluster operators

calc_posterior_single

Calculate a posterior value of theta for a single item

Load item paramaters

Calculate hyperparameters for log-normal distribution

Load set/stimulus/passage attributes

calc_likelihood_function

Calculate a likelihood function of theta

checkConstraints

Check the consistency of constraints and item usage

Calculate item response probabilities

Calculate probability at a single theta (GPC)

Draw RMSE plots

Calculate first derivative of log-likelihood

Calculate Fisher information at multiple thetas (GR)

Calculate probability at a single theta (GR)

Calculate an empirical Bayes estimate of theta for one examinee

theta_EAP_matrix

Calculate EAP estimates of theta for a group of examinees

Calculate the mutual information using full Bayesian

Find matching theta to supplied probability

Calculate Fisher information at a single theta (1PL)

dataset_reading

Reading dataset

Draw a shadow test chart

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 the Fisher information using full Bayesian

Calculate Fisher information at a single theta (2PL)

An S4 class to represent a partial credit item

item_pool-class

An S4 class to represent an item pool

makeItemPoolCluster

Create an item pool cluster object

plotExposureRateFinalFlag

Draw item information plots for flagged items by segment

Calculate hyperparameters for logit-normal distribution

constraint-class

An S4 class to represent a set of constraints

dataset_science

Science dataset

plotExposureRateFinal

Draw exposure rate plots by final theta segment

config_Shadow-class

createShadowTestConfig

theta_EB_single

Calculate an empirical Bayes estimate of theta for a single item

Draw item information plots

Create a subset of an item pool object

Simulate item responses

An S4 class to represent a 1PL item

iparPosteriorSample

Sample item parameter estimates from their posterior distributions

An S4 class to represent a 2PL item

An S4 class to represent a 3PL item

theta_FB_single

Calculate a fully Bayesian estimate of theta for a single item

extract-methods

output_Shadow-class

Find the segment to which each theta value belongs

Calculate a fully Bayesian estimate of theta for an examinee

Draw an audit trail plot

Calculate probability at a single theta (1PL)

Calculate probability at a single theta (PC)

loadConstraints

Load constraints

updateConstraints

Update constraints

Load item attributes

pool_cluster-class

An S4 class to represent a cluster of item pools

Create a subset of a test object

An S4 class to represent a test

Save or print audit trails

Generate a test object

makeTestCluster

Generate a test cluster object

Calculate probability at a single theta (2PL)

Calculate an EAP estimate of theta for one examinee

Draw a plot of maximum attainable information given the imposed constraints

plotInfoOverlay

Overlay item information plots

test_cluster-class

An S4 class to represent a test cluster

Calculate probability at a single theta (3PL)

Launch Shiny app

Calculate Fisher information at multiple thetas (1PL)

Calculate Root Mean Squared Error

Calculate Fisher information at multiple thetas (3PL)

Calculate Fisher information at multiple thetas (2PL)

Add transparancy to color

Perform shadow test assembly

Run Automated Test Assembly

Calculate Relative Errors