Package: OHPL 1.4.1

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.zip(r-4.5)OHPL_1.4.1.zip(r-4.4)OHPL_1.4.1.zip(r-4.3)
OHPL_1.4.1.tgz(r-4.4-any)OHPL_1.4.1.tgz(r-4.3-any)
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)
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Bug tracker:https://github.com/nanxstats/ohpl/issues

On CRAN:

chemometricshigh-dimensional-datahomogeneity-pursuitlassopartial-least-squares-regressionspectroscopyvariable-selection

7 exports 7 stars 1.41 score 12 dependencies 9 scripts 401 downloads

Last updated 2 months agofrom:4046c12737. Checks:OK: 7. Indexed: yes.

Exports:cv.OHPLdlcFOPOHPLOHPL.RMSEPOHPL.simproto

Dependencies:codetoolsforeachglmnetiteratorslatticeMatrixmvtnormplsRcppRcppEigenshapesurvival