Added a new function, find_cut(), which identifies
TIF-crossing routing cut scores for MST panels. For each adjacent module
pair within a stage, the function locates the theta where the two
modules’ test information functions (TIFs) intersect. A warning is
issued when the mean difficulty order of modules within a stage differs
from their input index order.
Added a new S3 method, plot.find_cut(), which
visualizes TIF curves and routing cut scores stage by stage using
ggplot2 facets. Proper, anomalous, and unselected cut
scores are distinguished by line type. A layout argument
("vertical" / "horizontal") controls the facet
orientation.
Added a new function, run_mst(), which simulates MST
administrations for a given panel structure and returns response data
along with ability and routing information for each simulated
examinee.
Added a new exported dataset, simIPD, which contains
simulated CAT response data for illustrating IPD detection with
ripd() and pcd2(). The dataset represents one
replication of a CAT simulation (N = 3,000; test length = 30; 360-item
3PLM pool) in which 5% of items (18 items) had both \(a\) and \(b\) parameters decreased by 0.5.
est_irt(), est_item(), and
est_mg() now accept a partial control list.
Users can specify only the arguments they wish to override (e.g.,
control = list(iter.max = 500)); unspecified arguments fall
back to their defaults via modifyList().
Reorganized the pkgdown reference page:
ripd() and pcd2() are now grouped under a new
Item Parameter Drift (IPD) section, and
reval_mst(), panel_info(),
find_cut(), plot.find_cut(), and
run_mst() are grouped under a new Multistage-Adaptive
Test (MST) section.
Expanded the documentation for ripd() by adding a
@details section that covers the theoretical background of
the RIPD framework, asymptotic distributions of the three RIPD
statistics, a drift-type diagnostic guide, the CAT-specific three-step
workflow, and the purification procedure. Also added a
\donttest{} example demonstrating a complete CAT-based IPD
detection workflow using the simIPD dataset. Updated
@references to include Lim & Choe (2023) and replaced
the previous conference paper citation with the in-press journal
reference (Lim & Han, in press).
Added a \donttest{} example to pcd2()
demonstrating CAT-based IPD detection using the simIPD
dataset, including the bootstrap critical value procedure described in
Lim & Han (in press).
Updated the MST Panel Evaluation and Simulation article
(vignettes/articles/mst-panel-evaluation.Rmd) to introduce
run_mst() and extend the existing reval_mst()
content with routing and scoring examples, a
find_cut()-based principled cut score derivation, a
side-by-side routing method comparison, and a Monte Carlo-vs-analytical
validation example (Example 6).
simMST dataset: the previous version had 9
items duplicated across non-adjacent modules because the original
assembly only enforced no-overlap within a single routing pathway. The
new version enforces a global no-overlap constraint across all 7 modules
(56 unique items total) and adds a mean(b) per-module band
constraint; cut scores were regenerated via find_cut() on
the rebuilt modules.est_irt(),
est_item(), and est_mg().est_score() by up
to 52% for dichotomous items (N = 10,000) and up to 27% for mixed-format
tests, through a series of optimizations.sx2_fit()
substantially by replacing the O(J^2) Lord-Wingersky recursion with a
forward-backward pass (up to 11x faster for mixed-format tests with J =
55 items) and vectorizing internal helper functions
expFreq(), obsFreq(), and the PRM
category-collapsing routine.testthat >= 3.0.0). Tests cover core functions
including drm(), prm(),
est_irt(), est_score(), est_mg(),
rdif(), crdif(), and catsib(),
with the relevant tests across dichotomous, polytomous, and mixed-format
item scenarios (355 tests total).ripd(), which implements the
Residual-based Item Parameter Drift (RIPD) detection framework. The
function computes three RIPD statistics: \(RIPD_R\), \(RIPD_S\), and \(RIPD_{RS}\) (one for each item). \(RIPD_R\) captures uniform item parameter
drift (IPD) via differences in mean raw residuals between groups, \(RIPD_S\) captures nonuniform IPD via
differences in mean squared residuals, and \(RIPD_{RS}\) is a combined chi-square-based
statistic sensitive to both types of drift. An optional purification
procedure is also supported.est_irt(), est_item(), and
est_mg().est_score().irtfit() and
sx2_fit() to assess model fit.rdif(),
grdif(), and catsib() to detect item
bias.cac_lee() and cac_rud().info(),
traceline(), lwrc(), simdat(),
and related helpers.reval_mst():
measurement precision and bias evaluation for multistage adaptive
tests.sx2_fit() that caused incorrect
cell collapsing between two adjacent score categories for polytomous
items when computing the S-\(X^2\) item
fit statistic.est_score() and
info().catsib() where the function failed
when all responses were missing (NA) in either the reference or focal
group.cac_rud() to include the x
argument, allowing users to pass item metadata data frames
directly.control parameters in
est_irt(), est_item(), and
est_mg(), and updated the documentation accordingly.est_score() function in terms of
Newton-Raphson method.catsib():
catsib_item() used a
hardcoded threshold of 3 instead of the user-supplied
min.binsize argument, causing inconsistent bin filtering
behavior.rho2 in
catsib_one() was not clamped to \([0, 1]\), so when
errvar > sigma2 (e.g., very few items or purification
cascade), a negative rho2 reversed the regression
correction direction, inflating the Type I error rate.errvar >= sigma2 during purification,
rho2 collapsed to 0, causing all corrected scores to
converge to the group mean. With group mean differences (impact), this
produced empty bin data frames and an invalid purification result. A
minimum floor of 0.05 is now enforced for rho2 to preserve
score spread.covirt() where the guessing
parameter (par[,3]) was incorrectly passed as the
difficulty parameter (par[,2]) to the
integrand() function for DRM items. This caused the
gradient computation to receive c = b, which zeroed out the
\(\partial P/\partial a\) and \(\partial P/\partial b\) gradient components
and produced a singular Fisher information matrix. Additionally,
NA values in par.3 for 1PLM and 2PLM items are
now substituted with 0 prior to gradient evaluation to prevent
NA propagation.The documentation for the irtQ package has been
revised to reflect updates to function behavior, fix typos, and provide
more relevant and detailed information for existing functions.
A new function, crdif(), has been added. This
function computes three statistics from the residual-based DIF detection
framework using categorical residuals (RDIF-CR). It allows for the
detection of global DIF, particularly in polytomously scored
items.
A new function, shape_df_fipc(), has been
introduced. This function merges fixed-item metadata with automatically
generated metadata for new items and produces a single data frame
ordered by test position. It is designed to support fixed item parameter
calibration (FIPC) via the est_irt() function.
The plot() method has been enhanced to support the
display of all item characteristic curves for a given item in a single
panel.
The pcd2() function has been updated to include a
purification procedure.
The rdif() and catsib() functions now
include an item.skip argument. This allows users to specify
a numeric vector of item indices to exclude from the DIF
analysis.
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 computes 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.
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