lvmcomp: Stochastic EM Algorithms for Latent Variable Models with a High-Dimensional Latent Space

Provides stochastic EM algorithms for latent variable models with a high-dimensional latent space. So far, we provide functions for confirmatory item factor analysis based on the multidimensional two parameter logistic (M2PL) model and the generalized multidimensional partial credit model. These functions scale well for problems with many latent traits (e.g., thirty or even more) and are virtually tuning-free. The computation is facilitated by multiprocessing 'OpenMP' API. For more information, please refer to: Zhang, S., Chen, Y., & Liu, Y. (2018). An Improved Stochastic EM Algorithm for Large-scale Full-information Item Factor Analysis. British Journal of Mathematical and Statistical Psychology. <doi:10.1111/bmsp.12153>.

Getting started

Package details

AuthorSiliang Zhang [aut, cre], Yunxiao Chen [aut], Jorge Nocedal [cph], Naoaki Okazaki [cph]
MaintainerSiliang Zhang <[email protected]>
LicenseGPL-3
Version1.2
URL https://github.com/slzhang-fd/lvmcomp
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("lvmcomp")

Try the lvmcomp package in your browser

Any scripts or data that you put into this service are public.

lvmcomp documentation built on May 1, 2019, 9:15 p.m.