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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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.