Package: FRESHD 1.0

FRESHD: Fast Robust Extraction of Signals from Heterogenous Data

Procedure for solving the maximin problem for identical design across heterogeneous data groups. Particularly efficient when the design matrix is either orthogonal or has tensor structure. Orthogonal wavelets can be specified for 1d, 2d or 3d data simply by name. For tensor structured design the tensor components (two or three) may be supplied. The package also provides an efficient implementation of the generic magging estimator.

Authors:Adam Lund

FRESHD_1.0.tar.gz
FRESHD_1.0.zip(r-4.5)FRESHD_1.0.zip(r-4.4)FRESHD_1.0.zip(r-4.3)
FRESHD_1.0.tgz(r-4.4-x86_64)FRESHD_1.0.tgz(r-4.4-arm64)FRESHD_1.0.tgz(r-4.3-x86_64)FRESHD_1.0.tgz(r-4.3-arm64)
FRESHD_1.0.tar.gz(r-4.5-noble)FRESHD_1.0.tar.gz(r-4.4-noble)
FRESHD_1.0.tgz(r-4.4-emscripten)FRESHD_1.0.tgz(r-4.3-emscripten)
FRESHD.pdf |FRESHD.html✨
FRESHD/json (API)

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

Peer review:

Bug tracker:https://github.com/adam-lund/freshd/issues

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

On CRAN:

2.00 score 146 downloads 5 exports 4 dependencies

Last updated 3 years agofrom:d306fb69e7. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-win-x86_64NOTENov 05 2024
R-4.5-linux-x86_64NOTENov 05 2024
R-4.4-win-x86_64NOTENov 05 2024
R-4.4-mac-x86_64NOTENov 05 2024
R-4.4-mac-aarch64NOTENov 05 2024
R-4.3-win-x86_64NOTENov 05 2024
R-4.3-mac-x86_64NOTENov 05 2024
R-4.3-mac-aarch64NOTENov 05 2024

Exports:iwtmaggingmaximinRHwt

Dependencies:glamlassoRcppRcppArmadilloRcppEigen