High-Performance Open-Source Archive
MAP method.STOC method.LSTM, implemented through NN.LSTM data.datasets.LSTM and the normalizing
scale data.scaler.LSTM.load.scaler and load.NN to
extract data and models from different neural networks, and removed
load_scaler and load_DNN.DNN
data.datasets and the normalizing scale
data.scaler to data.datasets.DNN and
data.scaler.DNN.DNN_predictor into NN, enabling it to use
different neural network models for factor retention.extractor.feature.FNN into
extractor.feature.NN, enabling it to extract features for
different neural network models.. globally instead of the underscore
_.CD used
summation. Although this did not affect the significance test, it has
been updated to use the mean to align perfectly with the theoretical
formulation.print and plot.factor.analysis in
version 1.2.3, methods involving factor.analysis may
produce different outputs compared to versions prior to 1.2.3, such as
CD and FF. Please take the results from the
new version as the standard.factor.analysis only
returned nfact eigenvalues.Examples have
been removed to avoid excessive time consumption during automatic
checks.factor.analysis may be inverted.CDF function code to improve its
execution efficiency.GenData when the
number of questions is very large.src/Makevars to achieve better
portability.ParamHelpers, has been rewritten based on version 1.14.1 to
support the FF function and no longer depends on the R
package ParamHelpers.EFAsim.data no longer requires the
parameter seed.check_python_libraries has been
provided to help users check if the Python libraries numpy
and onnxruntime are missing, and users can easily install
them using this function when they are not present.Hull.DESCRIPTION field.EFAscreet, Hull, and KGC.DESCRIPTION.Hull plot.PA.
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