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)
Build constraints
Calculate Fisher information at multiple thetas (1PL)
Calculate second derivative of log-likelihood
Calculate item response probabilities
Calculate item location
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
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)
createShadowTestConfig
Calculate probability at a single theta (GPC)
Calculate Fisher information at a single theta (GR).
Generate maximum likelihood estimates of theta
An S4 class to represent a set of constraints
Calculate the Fisher information using full Bayesian
Generate expected a posteriori estimates of theta
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
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)
Load constraints
Draw item eligibility statistics plots
Create an item pool cluster object
Load item attributes
Calculate hyperparameters for logit-normal distribution
Calculate first derivative
Calculate a posterior value of theta for a single item
Calculate a log-likelihood value of theta
Calculate second derivative
calc_log_likelihood_function
Calculate a log-likelihood function of theta
Calculate Fisher information at a single theta (GPC).
Fatigue dataset
createStaticTestConfig
Calculate Fisher information at a single theta (3PL)
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)
Check the consistency of constraints and item usage
Calculate a posterior value of theta
Reading dataset
Calculate a posterior distribution of theta
Science dataset
Generate a test object
An S4 class to represent an item pool
Generate a test cluster object
Extract
Draw an item exposure plot
An S4 class to represent a 2PL item
Calculate probability at a single theta (2PL)
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 and pool cluster operators
Calculate Fisher information at a single theta (2PL)
Calculate probability at a single theta (1PL)
output_Shadow
Overlay item information plots
plotExposureRateFinalFlag
Draw item information plots for flagged items by segment
plotExposureRateBySegment
Draw exposure rate plots by theta segment
Draw exposure rate plots by final theta segment
Draw a plot of maximum attainable information given the imposed constraints
Load set/stimulus/passage attributes
An S4 class to represent a cluster of item pools
Draw an audit trail plot
Calculate probability at a single theta (PC)
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
Calculate an empirical Bayes estimate of theta for a single item
Calculate a fully Bayesian estimate of theta for an examinee
Update constraints