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