Package: SMME 1.1.1

SMME: Soft Maximin Estimation for Large Scale Heterogeneous Data

Efficient procedure for solving the soft maximin problem for large scale heterogeneous data, see Lund, Mogensen and Hansen (2022) <doi:10.1111/sjos.12580>. Currently Lasso and SCAD penalized estimation is implemented. Note this package subsumes and replaces the SMMA package.

Authors:Adam Lund [aut, cre]

SMME_1.1.1.tar.gz
SMME_1.1.1.zip(r-4.5)SMME_1.1.1.zip(r-4.4)SMME_1.1.1.zip(r-4.3)
SMME_1.1.1.tgz(r-4.4-x86_64)SMME_1.1.1.tgz(r-4.4-arm64)SMME_1.1.1.tgz(r-4.3-x86_64)SMME_1.1.1.tgz(r-4.3-arm64)
SMME_1.1.1.tar.gz(r-4.5-noble)SMME_1.1.1.tar.gz(r-4.4-noble)
SMME_1.1.1.tgz(r-4.4-emscripten)SMME_1.1.1.tgz(r-4.3-emscripten)
SMME.pdf |SMME.html
SMME/json (API)

# Install 'SMME' in R:
install.packages('SMME', repos = c('https://adam-lund.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 163 downloads 1 mentions 5 exports 2 dependencies

Last updated 2 years agofrom:2d6fd72cd3. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 01 2024
R-4.5-win-x86_64NOTENov 01 2024
R-4.5-linux-x86_64NOTENov 01 2024
R-4.4-win-x86_64NOTENov 01 2024
R-4.4-mac-x86_64NOTENov 01 2024
R-4.4-mac-aarch64NOTENov 01 2024
R-4.3-win-x86_64NOTENov 01 2024
R-4.3-mac-x86_64NOTENov 01 2024
R-4.3-mac-aarch64NOTENov 01 2024

Exports:iwtpredict.SMMERHsoftmaximinwt

Dependencies:RcppRcppArmadillo