Determining the Number of Factors in Exploratory Factor Analysis by LSTM R package LSTMfactors version 1.0.0 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. Software Depends: R (>= 4.3.0) Imports: reticulate, EFAfactors Haijiang Qin <haijiang133@outlook.com> Comprehensive R Archive Network (CRAN) Haijiang Qin [aut, cre, cph] (ORCID: <https://orcid.org/0009-0000-6721-5653>), Lei Guo [aut, cph] (ORCID: <https://orcid.org/0000-0002-8273-3587>) GPL-3 2025-07-07 application/tgz https://CRAN.R-project.org/package=LSTMfactors doi:10.32614/CRAN.package.LSTMfactors