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
createStaticTestConfig
Calculate the Fisher information using empirical Bayes
Run computerized adaptive testing with generalized shadow-test approach
Calculate probability at multiple thetas (1PL)
Calculate second derivative
Calculate probability at multiple thetas (GPC)
Calculate probability at multiple thetas (PC)
Calculate Fisher information at multiple thetas (GPC)
Build constraints
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)
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
Draw item eligibility statistics plots
Calculate a posterior value of theta
Calculate a posterior distribution of theta
Calculate a likelihood value of theta
Item pool and pool cluster operators
Calculate a posterior value of theta for a single item
Load item paramaters
Calculate hyperparameters for log-normal distribution
Load set/stimulus/passage attributes
Calculate a likelihood function of theta
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
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)
Reading dataset
Draw a shadow test chart
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
An S4 class to represent an item pool
Create an item pool cluster object
plotExposureRateFinalFlag
Draw item information plots for flagged items by segment
Calculate hyperparameters for logit-normal distribution
An S4 class to represent a set of constraints
Science dataset
Draw exposure rate plots by final theta segment
createShadowTestConfig
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
Sample item parameter estimates from their posterior distributions
An S4 class to represent a 2PL item
An S4 class to represent a 3PL item
Calculate a fully Bayesian estimate of theta for a single item
Extract
output_Shadow
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)
Load constraints
Update constraints
Load item attributes
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
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
Overlay item information plots
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