Package: SMMA 1.0.3
Adam Lund
SMMA: Soft Maximin Estimation for Large Scale Array-Tensor Models
Efficient design matrix free procedure for solving a soft maximin problem for large scale array-tensor structured models, see Lund, Mogensen and Hansen (2019) <arxiv:1805.02407>. Currently Lasso and SCAD penalized estimation is implemented.
Authors:
SMMA_1.0.3.tar.gz
SMMA_1.0.3.tar.gz(r-4.5-noble)SMMA_1.0.3.tar.gz(r-4.4-noble)
SMMA_1.0.3.tgz(r-4.4-emscripten)SMMA_1.0.3.tgz(r-4.3-emscripten)
SMMA.pdf |SMMA.html✨
SMMA/json (API)
| # Install 'SMMA' in R: |
| install.packages('SMMA', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 4 years agofrom:67649b6a82. Checks:OK: 1 NOTE: 1. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 17 2024 |
| R-4.5-linux-x86_64 | NOTE | Oct 17 2024 |
Exports:RHsoftmaximin
Dependencies:RcppRcppArmadillo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Make Prediction From a SMMA Object | predict.SMMA |
| Print Function for objects of Class SMMA | print.SMMA |
| The Rotated H-transform of a 3d Array by a Matrix | glamlasso_RH H RH Rotate |
| Soft Maximin Estimation for Large Scale Array Data with Known Groups | pga SMMA softmaximin |
