Bayesian Tensor Factorization for decomposition of tensor data sets using the trilinear CANDECOMP/PARAFAC (CP) factorization, with automatic component selection. The complete data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The method performs factorization for three-way tensor datasets and the inference is implemented with Gibbs sampling.

Contains several utility functions for manipulating tensor-valued data (centering, multiplication from a single mode etc.) and the implementations of the following blind source separation methods for tensor-valued data: 'tPCA', 'tFOBI', 'tJADE', k-tJADE', 'tgFOBI', 'tgJADE', 'tSOBI', 'tNSS.SD', 'tNSS.JD', 'tNSS.TD.JD', 'tPP' and 'tTUCKER'.

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>.

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.

Here we provide tools for the computation and factorization of high-dimensional
	     tensor products that are formed by smaller matrices. The methods are based on
	     properties of Kronecker products (Searle 1982, p. 265, ISBN-10: 0470009616).
	     We evaluated this methodology by benchmark testing and illustrated its use in
	     Gaussian Linear Models ('Lopez-Cruz et al., 2024') <doi:10.1093/g3journal/jkae001>.

Interface to 'TensorFlow' <https://www.tensorflow.org/>,
  an open source software library for numerical computation using data
  flow graphs. Nodes in the graph represent mathematical operations,
  while the graph edges represent the multidimensional data arrays
  (tensors) communicated between them. The flexible architecture allows
  you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop,
  server, or mobile device with a single 'API'. 'TensorFlow' was originally
  developed by researchers and engineers working on the Google Brain Team
  within Google's Machine Intelligence research organization for the
  purposes of conducting machine learning and deep neural networks research,
  but the system is general enough to be applicable in a wide variety
  of other domains as well.