"Lessons in Statistical Thinking" D.T. Kaplan (2014) 
    <https://dtkaplan.github.io/Lessons-in-statistical-thinking/> 
    is a textbook for a first or second course in statistics that embraces 
    data wrangling, causal reasoning, modeling, statistical adjustment, 
    and simulation. 'LSTbook' supports the student-centered, tidy, 
    pipeline-oriented computing style featured in the book.

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.

Constructs tables of counts and proportions out of data sets with possibility to insert tables to Excel, Word, HTML, and PDF documents. Transforms tables to data suitable for modelling. Features Gibbs sampling based log-linear (NB2) and power analyses (original by Oleksandr Ocheredko <doi:10.35566/isdsa2019c5>) for tabulated data.

A set of tools for forecasting the next step in a multidimensional setting using tensors.  In the examples, a forecast is made of sea surface temperatures of a geographic grid (i.e. lat/long).  Each observation is a matrix, the entries in the matrix and the sea surface temperature at a particular lattitude/longitude. Cates, J., Hoover, R. C., Caudle, K., Kopp, R., & Ozdemir, C. (2021) "Transform-Based Tensor Auto Regression for Multilinear Time Series Forecasting" in 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 461-466), IEEE <doi:10.1109/ICMLA52953.2021.00078>.