New Functions
distractor.check(): Detects implausible and improper
distractors in a Q-matrix for multiple-choice items (Chiu, Köhn, &
Wang, in press).Q.implausible(): Generates a Q-matrix containing
implausible MC item distractors from a proper and plausible
Q-matrix.Q.improper(): Generates a Q-matrix containing improper
MC item distractors from a proper and plausible Q-matrix.plot.GNPC(): S3 plot method for GNPC objects, providing
convergence tracking visualization for individual examinees.run_gnpc_app(): Launches an interactive Shiny
application demonstrating NPC, GNPC, and G-DINA workflows.
New Dataset
Q_Ozaki: A Q-matrix for 30 multiple-choice items
measuring 5 attributes from Ozaki (2015), including coded
distractors.
Improvements
- Added S3
print methods for GNPC,
NPC, QR, Q.completeness, and
distractor.check objects, providing structured and
informative summaries.
- Added convergence tracking option in
GNPC() via the
track.convergence argument.
- Improved input validation across functions via internal
CheckInput().
- Consolidated internal pattern generation for consistency across all
functions.
Dependency Changes
- Removed dependency on
NPCD. The TSQE()
function now uses the package’s own QR() function for
Q-matrix refinement instead of NPCD::Qrefine().
- Removed dependency on
SimDesign.
- Initial CRAN release (2024-09-23).
- Core nonparametric classification methods:
NPC() and
GNPC().
- Q-matrix tools:
Q.completeness(),
Q.generate(), QR(), TSQE(),
bestQperm().
- Evaluation metrics:
AAR(), PAR(),
RR(), correction.rate(),
retention.rate().