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lvmcomp  

Stochastic EM Algorithms for Latent Variable Models with a High-Dimensional Latent Space
View on CRAN: Click here


Download and install lvmcomp package within the R console
Install from CRAN:
install.packages("lvmcomp")

Install from Github:
library("remotes")
install_github("cran/lvmcomp")

Install by package version:
library("remotes")
install_version("lvmcomp", "1.2")



Attach the package and use:
library("lvmcomp")
Maintained by
Siliang Zhang
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-12-30
Latest Update: 2018-12-30
Description:
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. .
How to cite:
Siliang Zhang (2018). lvmcomp: Stochastic EM Algorithms for Latent Variable Models with a High-Dimensional Latent Space. R package version 1.2, https://cran.r-project.org/web/packages/lvmcomp. Accessed 22 Dec. 2024.
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Complete documentation for lvmcomp
Functions, R codes and Examples using the lvmcomp R package
Some associated functions: StEM_mirt . StEM_pcirt . data_sim_mirt . data_sim_pcirt . 
Some associated R codes: RcppExports.R . StEM_mirt.R . StEM_pcirt.R . data.R .  Full lvmcomp package functions and examples
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