# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "LSTMfactors" in publications use:' type: software license: GPL-3.0-only title: 'LSTMfactors: Determining the Number of Factors in Exploratory Factor Analysis by LSTM' version: 1.0.0 doi: 10.32614/CRAN.package.LSTMfactors abstract: A method for factor retention using a pre-trained Long Short Term Memory (LSTM) Network, which is originally developed by Hochreiter and Schmidhuber (1997) , 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. authors: - family-names: Qin given-names: Haijiang email: haijiang133@outlook.com orcid: https://orcid.org/0009-0000-6721-5653 - family-names: Guo given-names: Lei email: happygl1229@swu.edu.cn orcid: https://orcid.org/0000-0002-8273-3587 repository: https://haijiangq.r-universe.dev commit: 6eaff65b4fe18b73eab75b00e8640b90dadf94e2 url: https://haijiangqin.com/LSTMfactors/ date-released: '2025-06-25' contact: - family-names: Qin given-names: Haijiang email: haijiang133@outlook.com orcid: https://orcid.org/0009-0000-6721-5653