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  "Package": "adelie",
  "Title": "Group Lasso and Elastic Net Solver for Generalized Linear Models",
  "Version": "1.0.10",
  "Date": "2026-06-03",
  "Authors@R": "c(\nperson(\"James\", \"Yang\", role = c(\"aut\", \"cph\")),\nperson(\"Trevor\", \"Hastie\", email=\"hastie@stanford.edu\", role = c(\"aut\", \"cph\", \"cre\")),\nperson(\"Balasubramanian\", \"Narasimhan\", role = c(\"aut\")))",
  "Description": "Extremely efficient procedures for fitting the entire\ngroup lasso and group elastic net regularization path for GLMs,\nmultinomial, the Cox model and multi-task Gaussian models.\nSimilar to the R package 'glmnet' in scope of models, and in\ncomputational speed.  This package provides R bindings to the\nC++ code underlying the corresponding Python package 'adelie'.\nThese bindings offer a general purpose group elastic net\nsolver, a wide range of matrix classes that can exploit special\nstructure to allow large-scale inputs, and an assortment of\ngeneralized linear model classes for fitting various types of\ndata. The package is an implementation of Yang, J. and Hastie,\nT. (2024) <doi:10.48550/arXiv.2405.08631>.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
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  "SystemRequirements": "C++17",
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  "URL": "https://github.com/JamesYang007/adelie-r",
  "BugReports": "https://github.com/JamesYang007/adelie-r/issues",
  "Config/pak/sysreqs": "libicu-dev",
  "Repository": "https://jamesyang007.r-universe.dev",
  "Date/Publication": "2026-06-04 19:00:36 UTC",
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  "Author": "James Yang [aut, cph],\nTrevor Hastie [aut, cph, cre],\nBalasubramanian Narasimhan [aut]",
  "Maintainer": "Trevor Hastie <hastie@stanford.edu>",
  "_user": "jamesyang007",
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  "_created": "2026-07-04T09:20:04.000Z",
  "_published": "2026-07-04T10:03:06.171Z",
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    "message": "Merge pull request #26 from JamesYang007/zero-variance\n\nZero variance",
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    "name": "James Yang",
    "followers": 64,
    "description": "Just a PhD student who likes statistics and computer-science."
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  "_searchresults": 13,
  "_topics": [
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    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
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    "extra/contents.json",
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      "date": "2024-06-17"
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      "date": "2024-06-20"
    },
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    "cv.glintnet",
    "cv.grpnet",
    "gaussian_cov",
    "glintnet",
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    "glm.gaussian",
    "glm.multigaussian",
    "glm.multinomial",
    "glm.poisson",
    "grpnet",
    "io.snp_phased_ancestry",
    "io.snp_unphased",
    "matrix.block_diag",
    "matrix.concatenate",
    "matrix.convex_relu",
    "matrix.dense",
    "matrix.eager_cov",
    "matrix.interaction",
    "matrix.kronecker_eye",
    "matrix.lazy_cov",
    "matrix.one_hot",
    "matrix.snp_phased_ancestry",
    "matrix.snp_unphased",
    "matrix.sparse",
    "matrix.standardize",
    "matrix.subset",
    "plot.grpnet",
    "predict.glintnet",
    "predict.grpnet",
    "print.cv.grpnet",
    "set_configs"
  ],
  "_help": [
    {
      "page": "constraint.box",
      "title": "Create a box constraint for a group.",
      "topics": [
        "constraint.box"
      ]
    },
    {
      "page": "cv.glintnet",
      "title": "Cross-validation for glintnet",
      "topics": [
        "cv.glintnet"
      ]
    },
    {
      "page": "cv.grpnet",
      "title": "Cross-validation for grpnet",
      "topics": [
        "cv.grpnet"
      ]
    },
    {
      "page": "gaussian_cov",
      "title": "Solves group elastic net via covariance method.",
      "topics": [
        "gaussian_cov"
      ]
    },
    {
      "page": "glintnet",
      "title": "fit a GLM interaction model with group lasso or group elastic-net regularization",
      "topics": [
        "glintnet"
      ]
    },
    {
      "page": "glm.binomial",
      "title": "Creates a Binomial GLM family object.",
      "topics": [
        "glm.binomial"
      ]
    },
    {
      "page": "glm.cox",
      "title": "Creates a Cox GLM family object.",
      "topics": [
        "glm.cox"
      ]
    },
    {
      "page": "glm.gaussian",
      "title": "Creates a Gaussian GLM family object.",
      "topics": [
        "glm.gaussian"
      ]
    },
    {
      "page": "glm.multigaussian",
      "title": "Creates a MultiGaussian GLM family object.",
      "topics": [
        "glm.multigaussian"
      ]
    },
    {
      "page": "glm.multinomial",
      "title": "Creates a Multinomial GLM family object.",
      "topics": [
        "glm.multinomial"
      ]
    },
    {
      "page": "glm.poisson",
      "title": "Creates a Poisson GLM family object.",
      "topics": [
        "glm.poisson"
      ]
    },
    {
      "page": "grpnet",
      "title": "fit a GLM with group lasso or group elastic-net regularization",
      "topics": [
        "grpnet"
      ]
    },
    {
      "page": "io.snp_phased_ancestry",
      "title": "IO handler for SNP phased, ancestry matrix.",
      "topics": [
        "io.snp_phased_ancestry"
      ]
    },
    {
      "page": "io.snp_unphased",
      "title": "IO handler for SNP unphased matrix.",
      "topics": [
        "io.snp_unphased"
      ]
    },
    {
      "page": "matrix.block_diag",
      "title": "Creates a block-diagonal matrix.",
      "topics": [
        "matrix.block_diag"
      ]
    },
    {
      "page": "matrix.concatenate",
      "title": "Creates a concatenation of the matrices.",
      "topics": [
        "matrix.concatenate"
      ]
    },
    {
      "page": "matrix.convex_relu",
      "title": "Creates a feature matrix for the convex relu problem.",
      "topics": [
        "matrix.convex_relu"
      ]
    },
    {
      "page": "matrix.dense",
      "title": "Creates a dense matrix object.",
      "topics": [
        "matrix.dense"
      ]
    },
    {
      "page": "matrix.eager_cov",
      "title": "Creates an eager covariance matrix.",
      "topics": [
        "matrix.eager_cov"
      ]
    },
    {
      "page": "matrix.interaction",
      "title": "Creates a matrix with pairwise interactions.",
      "topics": [
        "matrix.interaction"
      ]
    },
    {
      "page": "matrix.kronecker_eye",
      "title": "Creates a Kronecker product with an identity matrix.",
      "topics": [
        "matrix.kronecker_eye"
      ]
    },
    {
      "page": "matrix.lazy_cov",
      "title": "Creates a lazy covariance matrix.",
      "topics": [
        "matrix.lazy_cov"
      ]
    },
    {
      "page": "matrix.one_hot",
      "title": "Creates a one-hot encoded matrix.",
      "topics": [
        "matrix.one_hot"
      ]
    },
    {
      "page": "matrix.snp_phased_ancestry",
      "title": "Creates a SNP phased, ancestry matrix.",
      "topics": [
        "matrix.snp_phased_ancestry"
      ]
    },
    {
      "page": "matrix.snp_unphased",
      "title": "Creates a SNP unphased matrix.",
      "topics": [
        "matrix.snp_unphased"
      ]
    },
    {
      "page": "matrix.sparse",
      "title": "Creates a sparse matrix object.",
      "topics": [
        "matrix.sparse"
      ]
    },
    {
      "page": "matrix.standardize",
      "title": "Creates a standardized matrix.",
      "topics": [
        "matrix.standardize"
      ]
    },
    {
      "page": "matrix.subset",
      "title": "Creates a subset of the matrix along an axis.",
      "topics": [
        "matrix.subset"
      ]
    },
    {
      "page": "plot.cv.grpnet",
      "title": "plot the cross-validation curve produced by cv.glintnet",
      "topics": [
        "plot.cv.glintnet",
        "plot.cv.grpnet"
      ]
    },
    {
      "page": "plot.grpnet",
      "title": "plot coefficients from a \"grpnet\" object",
      "topics": [
        "plot.grpnet"
      ]
    },
    {
      "page": "predict.cv.glintnet",
      "title": "make predictions from a \"cv.glintnet\" object.",
      "topics": [
        "coef.cv.glintnet",
        "predict.cv.glintnet"
      ]
    },
    {
      "page": "predict.cv.grpnet",
      "title": "make predictions from a \"cv.grpnet\" object.",
      "topics": [
        "coef.cv.grpnet",
        "predict.cv.grpnet"
      ]
    },
    {
      "page": "predict.glintnet",
      "title": "make predictions from a \"glintnet\" object.",
      "topics": [
        "coef.glintnet",
        "predict.glintnet"
      ]
    },
    {
      "page": "predict.grpnet",
      "title": "make predictions from a \"grpnet\" object.",
      "topics": [
        "coef.grpnet",
        "predict.grpnet"
      ]
    },
    {
      "page": "print.cv.grpnet",
      "title": "print a cross-validated grpnet object",
      "topics": [
        "print.cv.grpnet"
      ]
    },
    {
      "page": "print.glintnet",
      "title": "Print a summary of the glintnet path at each step along the path.",
      "topics": [
        "print.glintnet"
      ]
    },
    {
      "page": "print.grpnet",
      "title": "print a grpnet object",
      "topics": [
        "print.grpnet"
      ]
    },
    {
      "page": "set_configs",
      "title": "Set configuration settings.",
      "topics": [
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      "source": "adelie.Rmd",
      "filename": "adelie.html",
      "title": "An Introduction to adelie",
      "author": "James Yang, Trevor Hastie and Balasubramanian Narasimhan",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction to Group Lasso and Elastic Net",
        "Single-Response Group Elastic Net",
        "Multi-Response Group Elastic Net",
        "Quickstart",
        "Gaussian Group Elastic Net",
        "Lasso",
        "Group Lasso",
        "GLM Group Elastic Net",
        "Multi-Response GLM Elastic Net",
        "Cox Models",
        "Constraints",
        "Non-negative Lasso",
        "Constraints on a single group",
        "Matrix",
        "Some Useful Matrix Methods",
        "Dense Matrix",
        "Sparse Matrix",
        "Concatenated Matrices",
        "Mixed variables and one-hot encoding",
        "Standardization",
        "Extended Examples",
        "Learning Interactions via Hierarchical Group-Lasso Regularization",
        "Simulation Setup",
        "Tools in adelie for fitting a glinternet model",
        "Details: manual construction of interaction model",
        "References"
      ],
      "created": "2024-06-13 17:52:02",
      "modified": "2025-02-28 16:51:55",
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