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.