tensorEVD
A Fast Algorithm to Factorize High-Dimensional Tensor Product Matrices
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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.5 |
rolling linux/jammy R-4.5 | tensorEVD_0.1.5.tar.gz |
1.6 MiB |
0.1.5 |
rolling linux/noble R-4.5 | tensorEVD_0.1.5.tar.gz |
1.6 MiB |
0.1.5 |
rolling source/ R- | tensorEVD_0.1.5.tar.gz |
1.5 MiB |
0.1.5 |
latest linux/jammy R-4.5 | tensorEVD_0.1.5.tar.gz |
1.6 MiB |
0.1.5 |
latest linux/noble R-4.5 | tensorEVD_0.1.5.tar.gz |
1.6 MiB |
0.1.5 |
latest source/ R- | tensorEVD_0.1.5.tar.gz |
1.5 MiB |
0.1.5 |
2026-04-23 source/ R- | tensorEVD_0.1.5.tar.gz |
0 B |
0.1.4 |
2025-04-20 source/ R- | tensorEVD_0.1.4.tar.gz |
1.5 MiB |