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>.

Version: 1.2
Depends: R (≥ 3.1)
Imports: Rcpp (≥ 0.12.17), coda (≥ 0.19-1), stats
LinkingTo: Rcpp, RcppArmadillo
Published: 2018-12-30
DOI: 10.32614/CRAN.package.lvmcomp
Author: Siliang Zhang [aut, cre], Yunxiao Chen [aut], Jorge Nocedal [cph], Naoaki Okazaki [cph]
Maintainer: Siliang Zhang <zhangsiliang123 at>
License: GPL-3
NeedsCompilation: yes
CRAN checks: lvmcomp results


Reference manual: lvmcomp.pdf


Package source: lvmcomp_1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): lvmcomp_1.2.tgz, r-oldrel (arm64): lvmcomp_1.2.tgz, r-release (x86_64): lvmcomp_1.2.tgz, r-oldrel (x86_64): lvmcomp_1.2.tgz


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