Skip to contents

VEMIRT: Variational Expectation Maximization for High-dimensional IRT Models

The goal of VEMIRT is to provide computationally efficient tools for high-dimensional data, large sample sizes, large item banks, and complex designs. The package contains several example datasets and functions for

  • Parallel analysis
  • Exploratory and confirmatory M2PL analysis
  • Exploratory and confirmatory M3PL analysis
  • Standard error estimates for confirmatory M2PL analysis
  • Bootstrap sampling and importance sampling correction for M2PL analysis
  • Differential item functioning detection for confirmatory M2PL analysis

Installation

To install this package from source:

  1. Windows users may need to install the Rtools and include the checkbox option of installing Rtools to their path for easier command line usage. Mac users may need to install Xcode command line tools by sudo xcode-select --install in the terminal, and then install GNU Fortran compiler. Most Linux distributions should already have up to date compilers (or if not they can be installed/updated easily).

  2. Install the devtools package (if necessary), and install the package from GitHub with

if (!require(devtools)) install.packages("devtools")
devtools::install_github("MAP-LAB-UW/VEMIRT", build_vignettes = T)
torch::install_torch()

Tutorial

After installing the VEMIRT package, open its tutorial by running

vignette("VEMIRT")

Shiny App

After installing the VEMIRT package, open its shiny app by running

VEMIRT::shinyVEMIRT()