NEWS.md

In wjakethompson/measr: Bayesian Psychometric Measurement Using 'Stan'

measr (development version)

• Updated reliability functionality to allow for the calculation of accuracy and consistency with different thresholds for determining attribute classifications.

• Added new measrfit() function for creating measrfit objects from Stan models that were not originally created with measr.

• Added aic() and bic() functions for calculating the Akaike and Bayesian information criteria, respectively, for models estimated with method = "optim".

measr 1.0.0

New documentation

• A new article on model evaluation has been added to the project website (https://measr.info).

• The model estimation article has been updated to use the same (simulated) data set as the model evaluation article.

• More detailed installation instructions have been added to the getting started vignette (#23).

• A case study demonstrating a full DCM-based analysis using data from the ECPE (?ecpe_data) has been added to the project website.

Minor improvements and fixes

• Fixed bug in the LCDM specification of constraints for level-3 and above interaction terms.

• Functions for evaluating estimated models (e.g., fit_ppmc(), reliability()) no longer recalculate indices if they have previously been saved to the model object. This behavior can be overwritten with force = TRUE.

• Updated Stan syntax to be compatible with the new array syntax (@andrjohns, #36)

• get_parameters() now preserves item identifiers by default. Items can be renamed with numbers (e.g., 1, 2, 3, ...) by setting rename_item = TRUE.

• measr now reexports functions from posterior for conducting mathematical operations on posterior::rvar() objects.

• Respondent estimates are now returned as posterior::rvar() objects when not summarized.

measr 0.3.1

• Added a NEWS.md file to track changes to the package.

New features

• Support for additional model specifications has been added (#10):
• The compensatory reparameterized unified model (C-RUM) can now be estimated by defining type = "crum" in the measr_dcm() function.
• Users can now drop higher order interactions from the loglinear cognitive diagnostic model (LCDM). A new argument for measr_dcm(), max_interaction, defines the highest order interactions to estimate. For example, max_interaction = 2 will estimate only intercepts, main effects, and two-way interactions.

• A new argument to measr_dcm(), attribute_structure allows users to specified either "unconstrained" relationships between attributes or "independent" attributes.

• Updated prior specifications:

• Users can now specify a prior distribution for the structural parameters that govern the base rates of class membership (#2).
• Safeguards were added to warn users when a specified prior is not defined for the chosen DCM sub-type. For example, an error is generated if a prior is defined for a slipping parameter, but the LCDM was chosen as the type of model to be estimated (#1).

Minor improvements and fixes

• Fixed bug with backend = "rstan" where warmup iterations could be more than the total iterations requested by the user if warmup iterations were not also specified (#6).

• Additional specifications were added to measr_extract() for extracting results from an estimated model.

wjakethompson/measr documentation built on April 12, 2025, 9:46 p.m.