Package index • measr

Data sets

ecpe_data ecpe_qmatrix: Examination for the Certificate of Proficiency in English

mdm_data mdm_qmatrix: MacReady & Dayton (1977) multiplication data

Model estimation

Priors

Define prior distributions for the model parameters.

measrprior() prior() prior_() prior_string(): Prior definitions for measr models

is_measrprior(): Checks if argument is a measrprior object

default_dcm_priors(): Default priors for diagnostic classification models

get_parameters(): Get a list of possible parameters

Model fitting

Estimate the model using Markov chain Monte Carlo or Stan’s optimizer.

measr_dcm(): Fit Bayesian diagnostic classification models

measrfit-class: Class measrfit of models fitted with the measr package

measrfit(): Create a measrfit object

as_measrfit(): Coerce objects to a measrfit

is_measrfit(): Check if argument is a measrfit object

Model evaluation

Reliability

Estimate the pattern- or attribute-level classification accuracy and consistency.

reliability(): Estimate the reliability of psychometric models

cdi(): Item, attribute, and test-level discrimination indices

Model fit

Evaluate the fit of the estimated model to the observed data.

fit_m2(<measrdcm>): Estimate the M₂ fit statistic for diagnostic classification models

fit_ppmc(): Posterior predictive model checks for assessing model fit

Model comparisons

Assess the relative fit of two competing models.

loo_compare(<measrfit>): Relative model fit comparisons

loo(<measrfit>): Efficient approximate leave-one-out cross-validation (LOO)

waic(<measrfit>): Widely applicable information criterion (WAIC)

loglik_array(): Extract the log-likelihood of an estimated model

Add evaluations to model objects

Add reliability, model fit, and model comparison information to an estimated model object.

add_criterion() add_reliability() add_fit() add_respondent_estimates(): Add model evaluation metrics model objects

Model applications

View and use an estimated model.

measr_extract(): Extract components of a measrfit object

predict(<measrdcm>): Posterior draws of respondent proficiency

Miscellaneous

create_profiles(): Generate mastery profiles