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  "Package": "lgspline",
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  "Title": "Lagrangian Multiplier Smoothing Splines for Smooth Function\nEstimation",
  "Version": "1.1.0",
  "Authors@R": "person(\"Matthew\", \"Davis\", email = \"matthewlouisdavis@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-9714-1018\"))",
  "Description": "Implements Lagrangian multiplier smoothing splines for\nflexible nonparametric regression and function estimation.\nProvides tools for fitting, prediction, and inference using a\nconstrained optimization approach to enforce smoothness.\nSupports generalized linear models, Weibull accelerated failure\ntime (AFT) models, Cox proportional hazards models, quadratic\nprogramming constraints, and customizable working-correlation\nstructures, with options for parallel fitting. The core spline\nconstruction builds on Ezhov et al. (2018)\n<doi:10.1515/jag-2017-0029>. Quadratic-programming and SQP\ndetails follow Goldfarb & Idnani (1983)\n<doi:10.1007/BF02591962> and Nocedal & Wright (2006)\n<doi:10.1007/978-0-387-40065-5>. For smoothing spline and\npenalized spline background, see Wahba (1990)\n<doi:10.1137/1.9781611970128> and Wood (2017)\n<doi:10.1201/9781315370279>. For variance-component and\ncorrelation-parameter estimation, see Searle et al. (2006)\n<ISBN:978-0470009598>. The default multivariate partitioning\nstep uses k-means clustering as in MacQueen (1967).",
  "License": "MIT + file LICENSE",
  "Language": "en-US",
  "URL": "https://github.com/matthewlouisdavisBioStat/lgspline",
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  "Repository": "https://matthewlouisdavisbiostat.r-universe.dev",
  "Date/Publication": "2026-05-14 13:02:19 UTC",
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    "get_centers",
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    "info_cox",
    "info_negbin",
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    "lgspline",
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    "lgspline_negbin",
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    "loglik_weibull",
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    "matmult_block_diagonal",
    "matmult_U",
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    "negbin_glm_weight_function",
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    },
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      "page": "coef.wald_lgspline",
      "title": "Extract Coefficients from a wald_lgspline Object",
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    },
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      "title": "Confidence Intervals for lgspline Coefficients",
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    },
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      "topics": [
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    },
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      "page": "cox_family",
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      "topics": [
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    },
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      "page": "cox_helpers",
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      "topics": [
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      "topics": [
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    },
    {
      "page": "Details",
      "title": "Lagrangian Multiplier Smoothing Splines: Mathematical Details",
      "topics": [
        "Details"
      ]
    },
    {
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      "title": "Print Closed-Form Fitted Equation from lgspline Model",
      "topics": [
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        "equation.lgspline",
        "print.equation"
      ]
    },
    {
      "page": "find_extremum",
      "title": "Find the Extremum of a Fitted lgspline",
      "topics": [
        "find_extremum"
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    },
    {
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      "topics": [
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    },
    {
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      "title": "Verification Examples for get_B",
      "topics": [
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    },
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      "title": "Generic for Numerical Integration",
      "topics": [
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        "integrate.default"
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    },
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      "title": "Compute Leave-One-Out Cross-Validated Predictions for Gaussian Response/Identity Link under Constraint",
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    {
      "page": "lgspline",
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      "topics": [
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    },
    {
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      "title": "Fit Cox Proportional Hazards Model via lgspline",
      "topics": [
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    },
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      "title": "Fit Negative Binomial Model via lgspline",
      "topics": [
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      "topics": [
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    {
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      "title": "Compute Cox Partial Log-Likelihood",
      "topics": [
        "loglik_cox"
      ]
    },
    {
      "page": "loglik_negbin",
      "title": "Compute Negative Binomial Log-Likelihood",
      "topics": [
        "loglik_negbin"
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    },
    {
      "page": "loglik_weibull",
      "title": "Compute Log-Likelihood for Weibull Accelerated Failure Time Model",
      "topics": [
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    },
    {
      "page": "logLik.lgspline",
      "title": "Extract Log-Likelihood from a Fitted lgspline",
      "topics": [
        "logLik.lgspline"
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    },
    {
      "page": "matinvsqrt",
      "title": "Calculate Matrix Inverse Square Root for Symmetric Matrices",
      "topics": [
        "matinvsqrt"
      ]
    },
    {
      "page": "matsqrt",
      "title": "Calculate Matrix Square Root for Symmetric Matrices",
      "topics": [
        "matsqrt"
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    },
    {
      "page": "negbin_dispersion_function",
      "title": "NB Dispersion Function",
      "topics": [
        "negbin_dispersion_function"
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    },
    {
      "page": "negbin_family",
      "title": "Negative Binomial Family for lgspline",
      "topics": [
        "negbin_family"
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    },
    {
      "page": "negbin_glm_weight_function",
      "title": "NB GLM Weight Function",
      "topics": [
        "negbin_glm_weight_function"
      ]
    },
    {
      "page": "negbin_helpers",
      "title": "Negative Binomial Regression Helpers for lgspline",
      "topics": [
        "negbin_helpers"
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    },
    {
      "page": "negbin_qp_score_function",
      "title": "NB Score Function for Quadratic Programming and Blockfit",
      "topics": [
        "negbin_qp_score_function"
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    },
    {
      "page": "negbin_schur_correction",
      "title": "NB Schur Correction",
      "topics": [
        "negbin_schur_correction"
      ]
    },
    {
      "page": "negbin_theta",
      "title": "Estimate Negative Binomial Shape Parameter",
      "topics": [
        "negbin_theta"
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    },
    {
      "page": "plot.lgspline",
      "title": "Plot Method for lgspline Objects",
      "topics": [
        "plot.lgspline"
      ]
    },
    {
      "page": "plot.wald_lgspline",
      "title": "Plot Method for wald_lgspline Objects",
      "topics": [
        "plot.wald_lgspline"
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    },
    {
      "page": "predict.lgspline",
      "title": "Predict Method for lgspline Objects",
      "topics": [
        "predict.lgspline"
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    },
    {
      "page": "print.lgspline",
      "title": "Print Method for lgspline Objects",
      "topics": [
        "print.lgspline"
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    },
    {
      "page": "print.summary.lgspline",
      "title": "Print Method for lgspline Summaries",
      "topics": [
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    },
    {
      "page": "print.wald_lgspline",
      "title": "Print Method for wald_lgspline Objects",
      "topics": [
        "print.wald_lgspline"
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    {
      "page": "prior_loglik",
      "title": "Log-Prior Distribution Evaluation for lgspline Models",
      "topics": [
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    },
    {
      "page": "process_qp",
      "title": "Prepare Quadratic Programming Constraints for lgspline",
      "topics": [
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    },
    {
      "page": "reml_grad_from_dV",
      "title": "Evaluate the REML gradient with respect to a single correlation parameter",
      "topics": [
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    },
    {
      "page": "std",
      "title": "Standardize Vector to Z-Scores",
      "topics": [
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    },
    {
      "page": "summary.lgspline",
      "title": "Summary Method for lgspline Objects",
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