Calculate Fisher information at multiple thetas (GR)
Calculate Fisher information at multiple thetas (PC)
Calculate probability at multiple thetas (2PL)
Calculate probability at multiple thetas (1PL)
Calculate item location
Calculate first derivative of log-likelihood
Calculate second derivative of log-likelihood
Calculate Fisher information
Build constraints
Calculate first derivative
Calculate item response probabilities
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 (PC)
Calculate expected scores
Calculate probability at multiple thetas (GR)
Calculate a posterior distribution of theta
calc_log_likelihood_function
Calculate a log-likelihood function of theta
Calculate second derivative
Find matching theta to supplied probability
Calculate probability at multiple thetas (3PL)
Calculate Fisher information at multiple thetas (2PL)
Calculate probability at multiple thetas (GPC)
Calculate a likelihood value of theta
Calculate a posterior value of theta for a single item
Calculate a posterior value of theta
Calculate Fisher information at a single theta (1PL)
An S4 class to represent a generalized partial credit item
Fatigue dataset
Create an item pool cluster object
An S4 class to represent a 3PL item
Draw item eligibility statistics plots
createStaticTestConfig
Calculate hyperparameters for logit-normal distribution
Print solution items
Generate expected a posteriori estimates of theta
Generate expected a posteriori estimates of theta
Calculate Fisher information at a single theta (GR).
An S4 class to represent a 1PL item
Generate a test object
Calculate Fisher information at a single theta (GPC).
An S4 class to represent a 2PL item
Calculate the Fisher information matrix for a single theta value and a set of items, potentially with a mixture of different models
Calculate a likelihood function of theta
Calculate a log-likelihood value of theta
Calculate the mutual information using full Bayesian
Check the consistency of constraints and item usage
Draw an item exposure plot
Calculate Fisher information at a single theta (2PL)
Calculate Fisher information at a single theta (3PL)
createShadowTestConfig
Calculate the Fisher information using empirical Bayes
An S4 class to represent a set of constraints
An S4 class to represent a single constraint
Draw RMSE plots
Calculate EAP estimates of theta for a group of examinees
Overlay item information plots
Calculate an empirical Bayes estimate of theta for one examinee
Extract
output_Shadow
Generate a test cluster object
Calculate probability at a single theta (1PL)
plotExposureRateBySegment
Draw exposure rate plots by theta segment
Calculate the Fisher information using full Bayesian
Draw exposure rate plots by final theta segment
An S4 class to represent a set of constraints.
An S4 class to represent an item pool
Find the segment to which each theta value belongs
Create a subset of a test object
An S4 class to represent a test
Calculate probability at a single theta (GPC)
Load item attributes
Load constraints
Reading dataset
Calculate probability at a single theta (GR)
Item pool and pool cluster operators
Calculate probability at a single theta (2PL)
Science dataset
Load item paramaters
Sample item parameter estimates from their posterior distributions
Calculate hyperparameters for log-normal distribution
Calculate Fisher information at a single theta (PC)
Calculate probability at a single theta (3PL)
Load set/stimulus/passage attributes
plotExposureRateFinalFlag
Draw item information plots for flagged items by segment
Draw item information plots
Run Test Assembly
Calculate an EAP estimate of theta for one examinee
An S4 class to represent a test cluster
Calculate probability at a single theta (PC)
Generate maximum likelihood estimates of theta
An S4 class to represent a partial credit item
An S4 class to represent a graded response item
Generate maximum likelihood estimates of theta
Save or print audit trails
Draw an audit trail plot
Draw a shadow test chart
An S4 class to represent a cluster of item pools
An S4 class to represent a set of constraints.
Create a subset of an item pool object
Update constraints
Calculate a fully Bayesian estimate of theta for a single item
Show constraints
Simulate item responses
Calculate an empirical Bayes estimate of theta for a single item
Calculate a fully Bayesian estimate of theta for an examinee
Launch Shiny app
Add transparancy to color
Run Static Test Assembly
Run adaptive test assembly.
Calculate Root Mean Squared Error
Calculate Fisher information at multiple thetas (1PL)
Calculate Relative Errors
Calculate Fisher information at multiple thetas (3PL)
Calculate Fisher information at multiple thetas (GPC)