Package: OHPL 1.4.1

Nan Xiao

OHPL: Ordered Homogeneity Pursuit Lasso for Group Variable Selection

Ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection proposed in Lin et al. (2017) <doi:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the homogeneity structure in high-dimensional data and enjoys the grouping effect to select groups of important variables automatically. This feature makes it particularly useful for high-dimensional datasets with strongly correlated variables, such as spectroscopic data.

Authors:You-Wu Lin [aut], Nan Xiao [aut, cre]

OHPL_1.4.1.tar.gz
OHPL_1.4.1.tar.gz(r-4.5-noble)OHPL_1.4.1.tar.gz(r-4.4-noble)
OHPL_1.4.1.tgz(r-4.4-emscripten)OHPL_1.4.1.tgz(r-4.3-emscripten)
OHPL.pdf |OHPL.html✨
OHPL/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/nanxstats/ohpl/issues

Datasets:
  • beer - The beer dataset
  • soil - The soil dataset
  • wheat - The wheat dataset

7 exports 0.09 score 12 dependencies 9 scripts 401 downloads

Last updated 2 months agofrom:ae86c25f46. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKAug 20 2024
R-4.5-linuxOKAug 20 2024

Exports:cv.OHPLdlcFOPOHPLOHPL.RMSEPOHPL.simproto

Dependencies:codetoolsforeachglmnetiteratorslatticeMatrixmvtnormplsRcppRcppEigenshapesurvival