The function is used to compute the pattern-wise agreement rate between two sets of
attribute profiles. They need to have the same dimensions.

Statistical tools for analyzing cognitive diagnosis (CD) data collected from small settings using the nonparametric classification (NPCD) framework. The core methods of the NPCD framework includes the nonparametric classification (NPC) method developed by Chiu and Douglas (2013) <DOI:10.1007/s00357-013-9132-9> and the general NPC (GNPC) method developed by Chiu, Sun, and Bian (2018) <DOI:10.1007/s11336-017-9595-4> and Chiu and Köhn (2019) <DOI:10.1007/s11336-019-09660-x>. An extension of the NPCD framework included in the package is the nonparametric method for multiple-choice items (MC-NPC) developed by Wang, Chiu, and Koehn (2023) <DOI:10.3102/10769986221133088>. Functions associated with various extensions concerning the evaluation, validation, and feasibility of the CD analysis are also provided. These topics include the completeness of Q-matrix, Q-matrix refinement method, as well as Q-matrix estimation.

Weixuan Xiao

NPCDTools

The Nonparametric Classification Methods for Cognitive Diagnosis

Chia-Yi Chiu

Xiran Wen

Yu Wang

PAR function

<dl><dt>x</dt>
<dd>One set of attribute profiles</dd>
<dt>y</dt>
<dd>The other set of attribute profiles</dd></dl>

Arguments

Pattern-wise agreement rate — PAR

<dl>

<dt>x</dt>
<dd>One set of attribute profiles</dd>


<dt>y</dt>
<dd>The other set of attribute profiles</dd>

</dl>

PAR: Pattern-wise agreement rate

Description

Usage

Value

Arguments