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  "Title": "Functions for Nonlinear Least Squares Solutions",
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  "Date": "2016-3-2",
  "Authors@R": "c( person(given = c(\"John\", \"C.\"), family = \"Nash\", role =\nc(\"aut\", \"cre\"), email = \"nashjc@uottawa.ca\") )",
  "Author": "John C. Nash [aut, cre]",
  "Maintainer": "John C. Nash <nashjc@uottawa.ca>",
  "Description": "Replacement for nls() tools for working with nonlinear\nleast squares problems. The calling structure is similar to,\nbut much simpler than, that of the nls() function. Moreover,\nwhere nls() specifically does NOT deal with small or zero\nresidual problems, nlmrt is quite happy to solve them. It also\nattempts to be more robust in finding solutions, thereby\navoiding 'singular gradient' messages that arise in the\nGauss-Newton method within nls(). The Marquardt-Nash approach\nin nlmrt generally works more reliably to get a solution,\nthough this may be one of a set of possibilities, and may also\nbe statistically unsatisfactory. Added print and summary as of\nAugust 28, 2012.",
  "License": "GPL-2",
  "NeedsCompilation": "no",
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    "User": "root"
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  "Date/Publication": "2016-03-04 23:57:39 UTC",
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    "message": "version 2016.3.2\n",
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      "date": "2012-08-12"
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      "date": "2012-08-13"
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    "model2jacfun",
    "model2resfun",
    "model2ssfun",
    "modgr",
    "modss",
    "nlfb",
    "nlxb",
    "wrapnls"
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      "page": "nlmrt-package",
      "title": "Tools for solving nonlinear least squares problems. UNDER DEVELOPMENT.",
      "topics": [
        "nlmrt-package",
        "nlmrt"
      ]
    },
    {
      "page": "coef.nlmrt",
      "title": "Output model coefficients for nlmrt object.",
      "topics": [
        "coef.nlmrt"
      ]
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    {
      "page": "model2grfun",
      "title": "Generate a gradient function from a nonlinear model expression and a vector of named parameters.",
      "topics": [
        "model2grfun"
      ]
    },
    {
      "page": "model2jacfun",
      "title": "Generate a Jacobian matrix function from a nonlinear model expression and a vector of named parameters.",
      "topics": [
        "model2jacfun"
      ]
    },
    {
      "page": "model2resfun",
      "title": "Generate a residual function from a nonlinear model expression and a vector of named parameters.",
      "topics": [
        "model2resfun"
      ]
    },
    {
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      "title": "Generate a sum of squares objective function from a nonlinear model expression and a vector of named parameters.",
      "topics": [
        "model2ssfun"
      ]
    },
    {
      "page": "modgr",
      "title": "Compute gradient from residuals and Jacobian.",
      "topics": [
        "modgr"
      ]
    },
    {
      "page": "modss",
      "title": "Compute gradient from residuals and Jacobian.",
      "topics": [
        "modss"
      ]
    },
    {
      "page": "nlfb",
      "title": "Nash variant of Marquardt nonlinear least squares solution via qr linear solver.",
      "topics": [
        "nlfb"
      ]
    },
    {
      "page": "nlxb",
      "title": "Nash variant of Marquardt nonlinear least squares solution via qr linear solver.",
      "topics": [
        "nlxb"
      ]
    },
    {
      "page": "print.nlmrt",
      "title": "Print method for an object of class nlmrt.",
      "topics": [
        "print.nlmrt"
      ]
    },
    {
      "page": "summary.nlmrt",
      "title": "Summary output for nlmrt object.",
      "topics": [
        "summary.nlmrt"
      ]
    },
    {
      "page": "wrapnls",
      "title": "Nash variant of Marquardt nonlinear least squares solution via qr linear solver.",
      "topics": [
        "wrapnls"
      ]
    }
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      "created": "2013-08-10",
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