Package slamR details

Structured Latent Attribute Models in R

This package implements fast algorithms to fit structured latent class models (SLAM) for high-dimensional dependent binary data (Gu and Xu, 2019,'JMLR'). SLAMs are a special family of discrete latent variable models widely used in social and biological sciences. The goal is to learn from high-dimensional data the signi<81>cant attribute patterns based on a SLAM with potentially high-dimensional con<81>gurations of the latent attributes. The algorithms perform selection of the attribute patterns, estimation of the unknown Q-matrix connecting the measurements to the latent attributes, and other model parametersincluding proportion parameters and response probability parameters.

Maintainer: Zhenke Wu < zhenkewu at umich.edu >

Documentation

From within R, enter citation('slamR')

To cite package 'slamR' in publications use:

Zhenke Wu, Yuqi Gu and Gongjun Xu (2020). slamR: Structured Latent
Attribute Models in R. R package version 0.2.1.
https://github.com/zhenkewu/slamR

A BibTeX entry for LaTeX users is

@Manual{,
title = {slamR: Structured Latent Attribute Models in R},
author = {Zhenke Wu and Yuqi Gu and Gongjun Xu},
year = {2020},
note = {R package version 0.2.1},
url = {https://github.com/zhenkewu/slamR},
}

If you have any problems with this package you can open a new issue or check the already existing ones here.

To install this package, start R and enter:

source("https://oslerinhealth.org/oslerLite.R")

# Default Install
osler_install('slamR')

# from GitHub
osler_install('slamR', release = "stable", release_repo = "github")
osler_install('slamR', release = "current", release_repo = "github")

More detailed installation instructions can be found here.