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 >


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.

A BibTeX entry for LaTeX users is

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 = {},


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

  Versions(Pending - no previous version)
To install this package, start R and enter:


# Default Install

# 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.


Initially submitted on April 28 2021 10:00AM
Last updated on April 28 2021 10:00AM
Package type standard
Source GitHub GitHub
OslerinHealth GitHub GitHub
DependsR (3.5.0)
ImportsmatrixStats (0.52.2), Rcpp (0.12.15), stats (3.4.1), graphics (3.4.1), grDevices (3.4.1)
LinkingToRcpp, RcppArmadillo
Suggestsknitr, testthat, bibtex, knitcitations, rmarkdown, ars (0.5), RcppArmadillo (0.8.300.1.0)