Calculate the Fisher information using full Bayesian
Calculate expected scores
Calculate second derivative of log-likelihood
Calculate Fisher information
(C++) For multiple items, calculate likelihoods
Check the consistency of constraints and item usage
Calculate first derivative of log-likelihood
Calculate central location (overall difficulty)
Calculate Relative Errors
Calculate a posterior value of theta
Calculate a posterior distribution of theta
Class 'constraint': a single constraint
Compute expected a posteriori estimates of theta
Run adaptive test assembly
(C++) Classify theta values into segments using cutpoints
Calculate Root Mean Squared Error
Detect best solver
Open TestDesign app
Calculate alpha angles from a-parameters
Create a config_Static object
Create a config_Shadow object
Calculate the mutual information using full Bayesian
(C++) Calculate expected scores
Reading dataset
Create an item pool cluster object
Calculate a posterior value of theta for a single item
(C++) For multiple items, calculate Fisher information
(C++) Calculate first derivative of log-likelihood
Class 'item_pool_cluster': an item pool
Class 'output_Static': fixed-form assembly solution
Basic operators for item pool objects
Convert mean and standard deviation into log-normal distribution parameters
Print solution items
Split an item pool into partitions
Retrieve constraints-related attributes from solution
Calculate log-likelihood
Calculate item response probabilities
Basic functions for item attribute objects
Class 'item_pool': an item pool
Science dataset
Class 'constraints': a set of constraints
Load set/stimulus/passage attributes
Convert mean and standard deviation into logit-normal distribution parameters
(C++) Calculate item response probability
Basic operators for constraints objects
Compute maximum likelihood estimates of theta using fence items
Create a simulation data cache object
Test solver
Load constraints
(C++) Calculate Fisher information
Load item attributes
(C++) Calculate second derivative of log-likelihood
Load item pool
Bayes dataset
Run Test Assembly
Extension of show() for objects in TestDesign package
Class 'output_Shadow': adaptive assembly solution for one simulee
Class 'test_cluster': data cache for simulations
Create a test object
Extension of summary() for objects in TestDesign package
Class 'test': data cache for simulations
(C++) Calculate a theta estimate using EB (Empirical Bayes) method
(C++) Calculate a theta estimate using FB (Full Bayes) method
Extension of plot() for objects in TestDesign package
Extension of print() for objects in TestDesign package
Fatigue dataset
Simulate item response data
Item classes
Generate item parameter samples using standard errors
Create a test cluster object
Class 'output_Shadow_all': a set of adaptive assembly solutions
Compute maximum likelihood estimates of theta
Basic operators for test objects
Class 'output_Split': partitioning solution
Summary classes
Basic functions for stimulus attribute objects
simulation_data_cache-class
Class 'simulation_data_cache': data cache for Shadow()
(C++) Calculate a theta estimate using EAP (expected a posteriori) method
Toggle constraints
Run fixed-form test assembly
Open TestDesign app
Build constraints (shortcut to other loading functions)
Calculate the Fisher information using empirical Bayes