Package: LSTMfactors 1.0.0

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.

Authors:Haijiang Qin [aut, cre, cph], Lei Guo [aut, cph]

LSTMfactors_1.0.0.tar.gz
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LSTMfactors_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
LSTMfactors/json (API)

# Install 'LSTMfactors' in R:

install.packages('LSTMfactors', repos = c('https://haijiangq.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

data.DAPCS - 20-item Dependency-Oriented and Achievement-Oriented Psychological Control Scale
data.datasets.LSTM - Subset Dataset for Training the Pre-Trained Long Short Term Memory (LSTM) Network
data.scaler.LSTM - The Scaler for the Pre-Trained Long Short Term Memory (LSTM) Network

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 231 downloads 7 exports 117 dependencies

Last updated from:6eaff65b4f. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	191
source / vignettes	OK	218
linux-release-x86_64	OK	187
macos-release-arm64	OK	176
macos-oldrel-arm64	OK	98
windows-devel	OK	116
windows-release	OK	119
windows-oldrel	OK	111
wasm-release	OK	166

Exports:af.softmax check_python_libraries extractor.feature load.LSTM load.scaler LSTM normalizor

Dependencies:askpass backports base64enc BBmisc bit bit64 bslib cachem checkmate cli clipr commonmark cpp11 crayon crosstalk curl data.table ddpcr digest dplyr DT EFAfactors evaluate evd farver fastmap fastmatch fontawesome fs generics ggplot2 glue GPArotation gtable here highr hms htmltools htmlwidgets httpuv httr ineq isoband jquerylib jsonlite kernlab knitr labeling later lattice lazyeval lifecycle magrittr MASS Matrix memoise mime mixtools mlr mnormt nlme openssl otel parallelMap ParamHelpers pillar pkgconfig plotly plyr png prettyunits progress promises proxy psych purrr R6 ranger rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen RcppTOML readr reticulate rlang rmarkdown rprojroot S7 sass scales segmented shiny shinydisconnect shinyjs SimCorMultRes sourcetools stringi stringr survival sys tibble tidyr tidyselect tinytex tzdb utf8 vctrs viridisLite vroom withr xfun xgboost XML xtable yaml

Help page	Topics
An Activation Function: Softmax	af.softmax
Check and Install Python Libraries (numpy and onnxruntime)	check_python_libraries
20-item Dependency-Oriented and Achievement-Oriented Psychological Control Scale (DAPCS)	data.DAPCS
Subset Dataset for Training the Pre-Trained Long Short Term Memory (LSTM) Network	data.datasets.LSTM
the Scaler for the Pre-Trained Long Short Term Memory (LSTM) Network	data.scaler.LSTM
Extracting features for the pre-trained Long Short Term Memory (LSTM) Network	extractor.feature
Load the pre-trained Long Short Term Memory (LSTM) Network	load.LSTM
Load the Scaler for the pre-trained Long Short Term Memory (LSTM) Network	load.scaler
A pre-trained Long Short Term Memory (LSTM) Network for Determining the Number of Factors	LSTM
Feature Normalization	normalizor
Plot LSTM Classification Probability Distribution	plot.LSTM
Print LSTM Results	print.LSTM

Package: LSTMfactors 1.0.0

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

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