Package: LSTMfactors
Type: Package
Title: Determining the Number of Factors in Exploratory Factor Analysis
        by LSTM
Version: 1.0.0
Date: 2025-06-25
Author: 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>)
Authors@R: c(person(given = "Haijiang", 
                    family = "Qin", 
                    role = c("aut", "cre", "cph"), 
                    email = "haijiang133@outlook.com", 
                    comment = c(ORCID = "0009-0000-6721-5653")),
             person(given = "Lei", 
                    family = "Guo", 
                    role = c("aut", "cph"), 
                    email = "happygl1229@swu.edu.cn", 
                    comment = c(ORCID = "0000-0002-8273-3587")))
Maintainer: Haijiang Qin <haijiang133@outlook.com>
Description: 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.
License: GPL-3
Depends: R (>= 4.3.0)
Imports: reticulate, EFAfactors
RoxygenNote: 7.3.2
Encoding: UTF-8
NeedsCompilation: yes
Collate: 'af.softmax.R' 'check_python_libraries.R' 'data.DAPCS.R'
        'data.datasets.LSTM.R' 'data.scaler.LSTM.R' 'LSTM.R'
        'extractor.feature.R' 'load.R' 'normalizor.R' 'plot.R'
        'print.R' 'utils.R' 'zzz.R'
Repository: CRAN
URL: https://haijiangqin.com/LSTMfactors/
Packaged: 2025-07-03 13:00:29 UTC; Haijiang
Date/Publication: 2025-07-07 12:50:12 UTC
Built: R 4.6.0; ; 2026-04-30 13:10:19 UTC; unix
