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  "Title": "Ordered Homogeneity Pursuit Lasso for Group Variable Selection",
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  "Description": "Ordered homogeneity pursuit lasso (OHPL) algorithm for\ngroup variable selection proposed in Lin et al. (2017)\n<DOI:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits\nthe homogeneity structure in high-dimensional data and enjoys\nthe grouping effect to select groups of important variables\nautomatically. This feature makes it particularly useful for\nhigh-dimensional datasets with strongly correlated variables,\nsuch as spectroscopic data.",
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  "URL": "https://ohpl.io, https://ohpl.io/doc/,\nhttps://github.com/nanxstats/OHPL",
  "BugReports": "https://github.com/nanxstats/OHPL/issues",
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  "Date/Publication": "2026-04-13 21:14:18 UTC",
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  "Author": "You-Wu Lin [aut],\nNan Xiao [aut, cre] (ORCID: <https://orcid.org/0000-0002-0250-5673>)",
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    "variable-selection"
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