{
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  "Version": "2019-12.4",
  "Date": "2019-12-04",
  "Title": "Tools to Support Optimization Possibly with Bounds and Masks",
  "Authors@R": "c( person(given = c(\"John\", \"C\"), family = \"Nash\", \nrole =c(\"aut\", \"cre\"), email = \"nashjc@uottawa.ca\") )",
  "Maintainer": "John C Nash <nashjc@uottawa.ca>",
  "Description": "Tools to assist in safely applying user generated\nobjective and derivative function to optimization programs.\nThese are primarily function minimization methods with at most\nbounds and masks on the parameters. Provides a way to check the\nbasic computation of objective functions that the user\nprovides, along with proposed gradient and Hessian functions,\nas well as to wrap such functions to avoid failures when\ninadmissible parameters are provided. Check bounds and masks.\nCheck scaling or optimality conditions. Perform an axial search\nto seek lower points on the objective function surface.\nIncludes forward, central and backward gradient approximation\ncodes.",
  "License": "GPL-2",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-17 05:40:01 UTC",
    "User": "root"
  },
  "Author": "John C Nash [aut, cre]",
  "Repository": "https://nashjc.r-universe.dev",
  "Date/Publication": "2019-12-20 12:20:07 UTC",
  "RemoteUrl": "https://github.com/cran/optextras",
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    "author": "John C Nash <nashjc@uottawa.ca>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 2019-12.4\n",
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  "_maintainer": {
    "name": "John C Nash",
    "email": "nashjc@uottawa.ca",
    "login": "nashjc",
    "description": "I do scientific and statistical computation, particularly function minimization and least squares.",
    "uuid": 3526572
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      "role": "Imports"
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      "package": "utils",
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  "_selfowned": true,
  "_usedby": 0,
  "_updates": [],
  "_tags": [],
  "_stars": 0,
  "_contributors": [
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      "count": 4,
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    "name": "John C. Nash",
    "description": "I do scientific and statistical computation, particularly function minimization and least squares."
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  "_assets": [
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    "extra/citation.json",
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    "extra/NEWS.txt",
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  "_releases": [
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      "date": "2013-11-08"
    },
    {
      "version": "2013-10.28",
      "date": "2014-12-05"
    },
    {
      "version": "2016-8.8",
      "date": "2016-08-08"
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      "date": "2019-12-20"
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  "_exports": [
    "axsearch",
    "bmchk",
    "bmstep",
    "ctrldefault",
    "fnchk",
    "gHgen",
    "gHgenb",
    "grback",
    "grcentral",
    "grchk",
    "grfwd",
    "grnd",
    "hesschk",
    "kktchk",
    "optsp",
    "scalechk"
  ],
  "_help": [
    {
      "page": "optextras-package",
      "title": "Tools to Support Optimization Possibly with Bounds and Masks",
      "topics": [
        "optextras"
      ]
    },
    {
      "page": "axsearch",
      "title": "Perform axial search around a supposed minimum and provide diagnostics",
      "topics": [
        "axsearch"
      ]
    },
    {
      "page": "bmchk",
      "title": "Check bounds and masks for parameter constraints used in nonlinear optimization",
      "topics": [
        "bmchk"
      ]
    },
    {
      "page": "bmstep",
      "title": "Compute the maximum step along a search direction.",
      "topics": [
        "bmstep"
      ]
    },
    {
      "page": "ctrldefault",
      "title": "set control defaults",
      "concept": [
        "minimization",
        "maximization"
      ],
      "topics": [
        "ctrldefault",
        "dispdefault"
      ]
    },
    {
      "page": "fnchk",
      "title": "Run tests, where possible, on user objective function",
      "topics": [
        "fnchk"
      ]
    },
    {
      "page": "gHgen",
      "title": "Generate gradient and Hessian for a function at given parameters.",
      "topics": [
        "gHgen"
      ]
    },
    {
      "page": "gHgenb",
      "title": "Generate gradient and Hessian for a function at given parameters.",
      "topics": [
        "gHgenb"
      ]
    },
    {
      "page": "grback",
      "title": "Backward difference numerical gradient approximation.",
      "topics": [
        "grback"
      ]
    },
    {
      "page": "grcentral",
      "title": "Central difference numerical gradient approximation.",
      "topics": [
        "grcentral"
      ]
    },
    {
      "page": "grchk",
      "title": "Run tests, where possible, on user objective function and (optionally) gradient and hessian",
      "topics": [
        "grchk"
      ]
    },
    {
      "page": "grfwd",
      "title": "Forward difference numerical gradient approximation.",
      "topics": [
        "grfwd",
        "optsp"
      ]
    },
    {
      "page": "grnd",
      "title": "A reorganization of the call to numDeriv grad() function.",
      "concept": [
        "minimization",
        "maximization"
      ],
      "topics": [
        "grnd"
      ]
    },
    {
      "page": "hesschk",
      "title": "Run tests, where possible, on user objective function and (optionally) gradient and hessian",
      "topics": [
        "hesschk"
      ]
    },
    {
      "page": "kktchk",
      "title": "Check Kuhn Karush Tucker conditions for a supposed function minimum",
      "concept": [
        "minimization",
        "maximization"
      ],
      "topics": [
        "kktchk"
      ]
    },
    {
      "page": "scalechk",
      "title": "Check the scale of the initial parameters and bounds input to an optimization code used in nonlinear optimization",
      "topics": [
        "scalechk"
      ]
    }
  ],
  "_rundeps": [
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