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-4.5-x86_64)FRESHD_1.0.tgz(r-4.5-arm64)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'))

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:

Conda:

openblascppopenmp

2.00 score 176 downloads 5 exports 4 dependencies

Last updated 3 years agofrom:d306fb69e7. Checks:1 OK, 11 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 05 2025
R-4.5-win-x86_64NOTEMar 05 2025
R-4.5-mac-x86_64NOTEMar 05 2025
R-4.5-mac-aarch64NOTEMar 05 2025
R-4.5-linux-x86_64NOTEMar 05 2025
R-4.4-win-x86_64NOTEMar 05 2025
R-4.4-mac-x86_64NOTEMar 05 2025
R-4.4-mac-aarch64NOTEMar 05 2025
R-4.4-linux-x86_64NOTEMar 05 2025
R-4.3-win-x86_64NOTEMar 05 2025
R-4.3-mac-x86_64NOTEMar 05 2025
R-4.3-mac-aarch64NOTEMar 05 2025

Exports:iwtmaggingmaximinRHwt

Dependencies:glamlassoRcppRcppArmadilloRcppEigen