Description: Performs model-based tensor clustering methods including Tensor Gaussian Mixture Model (TGMM), Tensor Envelope Mixture Model (TEMM) by Deng and Zhang (2021) <DOI: 10.1111/biom.13486>, Doubly-Enhanced EM (DEEM) algorithm by Mai, Zhang, Pan and Deng (2021) <DOI: 10.1080/01621459.2021.1904959>.

Title: Tensor Noise Reduction and Completion Methods
Author: Chanwoo Lee <chanwoo.lee@wisc.edu>, Miaoyan Wang <miaoyan.wang@wisc.edu>
Imports: pracma, methods, utils, tensorregress, MASS
Description: Efficient algorithms for tensor noise reduction and completion. This package includes a suite of parametric and nonparametric tools for estimating tensor signals from noisy, possibly incomplete observations. The methods allow a broad range of data types, including continuous, binary, and ordinal-valued tensor entries. The algorithms employ the alternating optimization. The detailed algorithm description can be found in the following three references.
URL: Chanwoo Lee and Miaoyan Wang. Tensor denoising and completion
based on ordinal observations. ICML, 2020.
Miaoyan Wang. Beyond the Signs: Nonparametric tensor completion
via sign series. 2021.  https://arxiv.org/abs/2102.00384
Chanwoo Lee, Lexin Li, Hao Helen Zhang, and Miaoyan Wang.
Nonparametric trace regression in high dimensions via sign
Packaged: 2021-05-10 19:16:04 UTC; chanwoolee

Title: Rank and Factor Loadings Estimation in Time Series Tensor Factor
Authors@R: c(person("Weilin", "Chen", role = c("aut", "cre"), email = "w.chen56@lse.ac.uk"))
Description: A set of functions to estimate rank and factor loadings of time series tensor factor models. A tensor is a multidimensional array. To analyze high-dimensional tensor time series, factor model is a major dimension reduction tool. 'TensorPreAve' provides functions to estimate the rank of core tensors and factor loading spaces of tensor time series. More specifically, a pre-averaging method that accumulates information from tensor fibres is used to estimate the factor loading spaces. The estimated directions corresponding to the strongest factors are then used for projecting the data for a potentially improved re-estimation of the factor loading spaces themselves. A new rank estimation method is also implemented to utilizes correlation information from the projected data.
See Chen and Lam (2022) <arXiv:2208.04012> for more details.
Packaged: 2022-11-07 23:32:03 UTC; William Chan