Enhanced functionality of the

`bind.fill()`

function by adding a new argument`fill`

. The value in the argument is used to fill in missing data when aligning datasets.Fixed a bug within the

`est_irt()`

function that was previously unable to implement the fixed item parameter calibration (FIPC) when only freely estimating a single item given that all other items are fixed.Added a new function,

`reval_mst()`

, which evaluates the measurement precision and bias in Multistage-adaptive Test (MST) panels using a recursion-based evaluation method introduced by Lim et al. (2020).Added a new function,

`pcd2()`

, which the Pseudo-count \(D^{2}\) statistics (Cappaert et al., 2018; Stone, 2000) to detect item parameter drift.

Introduced Warm’s (1989) Weighted Likelihood (WL) estimation method to the

`est_score()`

function. This WL scoring method can now be utilized by setting`method = "WL"`

.Enhanced the speed of ability parameter estimation in the

`est_score()`

function when using the ML, MLF, or MAP methods for the`method`

argument. The updated version performs approximately three times faster than its predecessor.Addressed a bug within the

`est_score()`

function that was previously unable to accurately compute scores when only a single item data was provided. This issue was occurring with the EAP.SUM and INV.TCC estimation methods.Added two new functions for computing classification accuracy and consistency:

`cac_rud()`

and`cac_lee()`

.`cac_rud`

: This function implements Rudner’s (2001, 2005) method for computing classification accuracy and consistency. It takes cut scores, ability estimates, standard errors, and optional weights as inputs and returns a list containing a confusion matrix, marginal and conditional classification accuracy and consistency indices, the probability of being assigned to each level category, and the cut scores used in the analysis.`cac_lee`

: This function implements Lee’s (2010) method for computing classification accuracy and consistency. It takes a data frame containing item metadata, cut scores, optional ability estimates, optional weights, a scaling factor, and a logical value indicating the cut score metric as inputs. It returns a list similar to`cac_rud`

.

Added a new function,

`llike_score()`

, which computes the loglikelihood of ability parameters given the item parameters and response data.Enhanced functionality of the

`rdif()`

and`grdif()`

functions: Both now support the graded response model (GRM) and generalized partial credit model (GPCM).Fixed an issue in the

`grdif()`

function that inaccurately calculated the GRDIF statistics when group membership was specified in a non-standard way. Specifically, the problem arose when 0 wasn’t used as the reference group and consecutive numbers (e.g., 1, 2, 3) weren’t used to represent focal groups in the`group`

argument.

Resolved the misalignment issue of standard errors in the output of the

`est_irt()`

function when`fix.a.1pl = TRUE`

is specified and the items are calibrated using the 1PLM.Added a new function,

`grdif()`

, to perform differential item functioning (DIF) analysis across multiple groups. This function calculates three generalized IRT residual DIF (GRDIF) statistics. For more information about the function and its usage, please refer to the accompanying documentation.Fixed several typos in the manual documentation

Initial release on CRAN

The

`irtQ`

package is a successor of the`irtplay`

package which was retracted from R CRAN due to the intellectual property (IP) violation. All issues of the IP violation have been clearly resolved in the`irtQ`

package.Most of the functions the

`irtQ`

package are identical in appearance and functionality to those of`irtplay`

package except a few functions (e.g.,`shape_df()`

,`est_score()`

). However, the computing speed of several functions (e.g.,`est_irt()`

,`est_score()`

,`lwrc()`

) in the`irtQ`

package are faster than the previous ones in the`irtplay`

package. Read the documentation carefully prior to using the functions.