LSTMfactors: Determining the Number of Factors in Exploratory Factor Analysis by LSTM

A method for factor retention using a pre-trained Long Short Term Memory (LSTM) Network, which is originally developed by Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>, is provided. The sample size of the dataset used to train the LSTM model is 1,000,000. Each sample is a batch of simulated response data with a specific latent factor structure. The eigenvalues of these response data will be used as sequential data to train the LSTM. The pre-trained LSTM is capable of factor retention for real response data with a true latent factor number ranging from 1 to 10, that is, determining the number of factors.

Version:	1.0.0
Depends:	R (≥ 4.3.0)
Imports:	reticulate, EFAfactors
Published:	2025-07-07
DOI:	10.32614/CRAN.package.LSTMfactors
Author:	Haijiang Qin [aut, cre, cph], Lei Guo [aut, cph]
Maintainer:	Haijiang Qin <haijiang133 at outlook.com>
License:	GPL-3
URL:	https://haijiangqin.com/LSTMfactors/
NeedsCompilation:	yes
Materials:	NEWS
CRAN checks:	LSTMfactors results

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