CRAN

mirtjml 1.2

Joint Maximum Likelihood Estimation for High-Dimensional Item Factor Analysis

Released Dec 21, 2018 by Siliang Zhang

This package can be loaded by Renjin but all tests failed.

Dependencies

RcppArmadillo 0.9.200.5.0 GPArotation 2014.11-1 Rcpp

Provides constrained joint maximum likelihood estimation algorithms for item factor analysis (IFA) based on multidimensional item response theory models. So far, we provide functions for exploratory and confirmatory IFA based on the multidimensional two parameter logistic (M2PL) model for binary response data. Comparing with traditional estimation methods for IFA, the methods implemented in this package scale better to data with large numbers of respondents, items, and latent factors. The computation is facilitated by multiprocessing 'OpenMP' API. For more information, please refer to: 1. Chen, Y., Li, X., & Zhang, S. (2018). Joint Maximum Likelihood Estimation for High-Dimensional Exploratory Item Factor Analysis. Psychometrika, 1-23. ; 2. Chen, Y., Li, X., & Zhang, S. (2017). Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications. arXiv preprint .

Installation

Maven

This package can be included as a dependency from a Java or Scala project by including the following your project's pom.xml file. Read more about embedding Renjin in JVM-based projects.

<dependencies>
  <dependency>
    <groupId>org.renjin.cran</groupId>
    <artifactId>mirtjml</artifactId>
    <version>1.2-b1</version>
  </dependency>
</dependencies>
<repositories>
  <repository>
    <id>bedatadriven</id>
    <name>bedatadriven public repo</name>
    <url>https://nexus.bedatadriven.com/content/groups/public/</url>
  </repository>
</repositories>

View build log

Renjin CLI

If you're using Renjin from the command line, you load this library by invoking:

library('org.renjin.cran:mirtjml')

Test Results

This package was last tested against Renjin 0.9.2716 on Dec 23, 2018.

Source

C++

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