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.

A set of functions that allow stationary analysis and locally stationary time series analysis.

Implementations of estimation algorithm of low rank plus sparse structured VAR model by using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). It relates to the algorithm in Sumanta, Li, and Michailidis (2019) <doi:10.1109/TSP.2018.2887401>.

Library of functions for the statistical analysis and simulation of Locally Stationary Wavelet Packet (LSWP) processes.  The methods implemented by this library are described in Cardinali and Nason (2017) <doi:10.1111/jtsa.12230>.

A word embeddings-based semi-supervised model for document scaling Watanabe (2020) <doi:10.1080/19312458.2020.1832976>.
    LSS allows users to analyze large and complex corpora on arbitrary dimensions with seed words exploiting efficiency of word embeddings (SVD, Glove).
    It can generate word vectors on a users-provided corpus or incorporate a pre-trained word vectors.

A lightweight grammar of tables. Build a table by declaring a JSON
    spec (titles, spanners, row groups, footnotes, formatting functions, etc.); a tiny
    vanilla JavaScript runtime builds the HTML table from the spec on page
    load. No 'sass', no 'V8', no 'htmlwidgets' — just base R and 'xfun'
    ('htmltools' is used only for the optional Shiny binding).