Implement the alternating algorithm for supervised tensor decomposition with interactive side information. Details can be found in the publication Hu, Jiaxin, Chanwoo Lee, and Miaoyan Wang. "Generalized Tensor Decomposition with features on multiple modes." Journal of Computational and Graphical Statistics, Vol. 31, No. 1, 204-218, 2022 <doi:10.1080/10618600.2021.1978471>.

Implements the multiway sparse clustering approach of M. Wang and Y. Zeng, "Multiway clustering via tensor block models". Advances in Neural Information Processing System 32 (NeurIPS), 715-725, 2019.

An implementation of fitting generalized linear models on
    second-order tensor type data. The functions within this package mainly focus on
    parameter estimation, including parameter coefficients and standard deviation.

Factor and autoregressive models for matrix and tensor valued time series. We provide functions for estimation, simulation and prediction. The models are discussed in 
    Li et al (2021) <doi:10.48550/arXiv.2110.00928>, Chen et al (2020) <DOI:10.1080/01621459.2021.1912757>, 
    Chen et al (2020) <DOI:10.1016/j.jeconom.2020.07.015>, and Xiao et al (2020) <doi:10.48550/arXiv.2006.02611>.

A collection of functions for Kronecker structured covariance
    estimation and testing under the array normal model. For estimation,
    maximum likelihood and Bayesian equivariant estimation procedures are
    implemented. For testing, a likelihood ratio testing procedure is
    available. This package also contains additional functions for manipulating
    and decomposing tensor data sets. This work was partially supported by NSF
    grant DMS-1505136. Details of the methods are described in
    Gerard and Hoff (2015) <doi:10.1016/j.jmva.2015.01.020> and
    Gerard and Hoff (2016) <doi:10.1016/j.laa.2016.04.033>.

The general principle relies on calculating the cumulative signal of nascent
    RNA sequencing over the gene body of any given gene or transcription unit.
    'tepr' can identify transcription attenuation sites by comparing profile to
    a null model which assumes uniform read density over the entirety of the
    transcription unit. It can also identify increased or diminished
    transcription attenuation by comparing two conditions. Besides rigorous
    statistical testing and high sensitivity, a major feature of 'tepr' is its
    ability to provide the elongation pattern of each individual gene, including
    the position of the main attenuation point when such a phenomenon occurs.
    Using 'tepr', users can visualize and refine genome-wide aggregated analyses
    of elongation patterns to robustly identify effects specific to subsets of
    genes. These metrics are suitable for internal comparisons (between genes in
    each condition) and for studying elongation of the same gene in different
    conditions or comparing it to a perfect theoretical uniform elongation.