{
  "_id": "6aae45de3d719c1f8407a814",
  "Package": "NPCirc",
  "Type": "Package",
  "Title": "Nonparametric Circular Methods",
  "Version": "3.2.1",
  "Date": "2026-02-19",
  "Authors@R": "c(person(given = \"Maria\",\nfamily = \"Alonso-Pena\",\nrole = c(\"aut\", \"cre\"),\nemail = \"mariaalonso.pena@usc.es\"),\nperson(given = \"Maria\",\nfamily = \"Oliveira\",\nrole = \"aut\"),\nperson(given = \"Jose\",\nfamily = \"Ameijeiras-Alonso\",\nrole = \"aut\"),\nperson(given = c(\"Rosa\", \"M.\"),\nfamily = \"Crujeiras\",\nrole = \"aut\"),\nperson(given = \"Irene\",\nfamily = \"Gijbels\",\nrole = \"aut\"),\nperson(given = \"Alberto\",\nfamily = \"Rodriguez-Casal\",\nrole = \"aut\"),\nperson(given = \"Felicita\",\nfamily = \"Scapini\",\nrole = \"dtc\"))",
  "Maintainer": "Maria Alonso-Pena <mariaalonso.pena@usc.es>",
  "Description": "Nonparametric smoothing methods for density and regression\nestimation and inference with circular data. The package\nprovides kernel density estimation along with inferential tools\nsuch as circular SiZer for feature significance, mode\nestimation, and modal clustering. It includes multiple methods\nfor selecting the smoothing parameter, allowing users to\noptimize the trade-off between bias and variance. Various\nplotting functions help visualize estimated densities, modes,\nclusters, and significance features. For regression, the\npackage implements nonparametric estimation of the mean\nregression function as well as other conditional\ncharacteristics, including modal regression and generalized\nregression. Bandwidth selection is also supported in the\nregression context, and testing procedures are available to\nassess structural features or effects in circular regression\nmodels.",
  "License": "GPL (>= 2)",
  "URL": "https://www.jstatsoft.org/v61/i09/",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-09-19 08:12:54 UTC",
    "User": "root"
  },
  "Author": "Maria Alonso-Pena [aut, cre], Maria Oliveira [aut], Jose\nAmeijeiras-Alonso [aut], Rosa M. Crujeiras [aut], Irene Gijbels\n[aut], Alberto Rodriguez-Casal [aut], Felicita Scapini [dtc]",
  "Config/pak/sysreqs": "cmake libfreetype6-dev libglu1-mesa-dev make\ntexlive libpng-dev libuv1-dev libgl1-mesa-dev zlib1g-dev",
  "Repository": "https://maria-alonsopena.r-universe.dev",
  "Date/Publication": "2026-02-20 06:40:22 UTC",
  "RemoteUrl": "https://github.com/cran/NPCirc",
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  "_expires": "2026-12-28T08:20:45.000Z",
  "_created": "2026-09-19T08:12:54.000Z",
  "_published": "2026-09-19T08:20:46.755Z",
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    "author": "Maria Alonso-Pena <mariaalonso.pena@usc.es>",
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    "message": "version 3.2.1\n",
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  "_topics": [
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    "bw.circ.local.lik",
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    "bw.dpi",
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    "bw.modalreg.circ.circ",
    "bw.modalreg.circ.lin",
    "bw.modalreg.lin.circ",
    "bw.pi",
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    "bw.reg.circ.lin",
    "bw.reg.lin.circ",
    "bw.rt",
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    "check_lwd",
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    "circ.local.lik",
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    "circ.modes",
    "circsizer.density",
    "circsizer.map",
    "circsizer.regression",
    "cosmoments",
    "cross_validation",
    "CRSC",
    "d_fun",
    "dcircmix",
    "dcirculardr",
    "ddvonmises",
    "DensCircRad",
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    "dpreg.circ",
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    "dvonmisesd1",
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    "dvonmisesd3",
    "dvonmisesd4",
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    "intP1",
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    "kern.den.circ",
    "kern.dpreg.circ",
    "kern.reg.circ.circ",
    "kern.reg.circ.lin",
    "kern.reg.lin.circ",
    "kernCL",
    "lines.regression.circular",
    "loc_loglik",
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    "matrixK_block",
    "MISECC",
    "MISECL",
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    "modal.clustering",
    "modalreg.circ.circ",
    "modalreg.circ.lin",
    "modalreg.lin.circ",
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    "noeffect.circ.circ",
    "noeffect.circ.lin",
    "noeffect.lin.circ",
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    "print.regression.circular",
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    "R_CV_grid_modereg_LinCirc",
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    "R_CV_modereg_CircLin2",
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    "R_cv_re_modereg_LinCirc",
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    "RegLinCirc",
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      "title": "Nonparametric circular methods.",
      "topics": [
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        "NPCirc"
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    },
    {
      "page": "ancova.circ.lin",
      "title": "Nonparametric analysis of covariance tests for circular regression",
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        "ancova.circ.circ",
        "ancova.circ.lin",
        "ancova.lin.circ"
      ]
    },
    {
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      "title": "Smoothing parameter selection for circular multimdodal regression",
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        "bw.modalreg.circ.lin",
        "bw.modalreg.lin.circ"
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      "title": "Local likelihood estimation for regression with circular covariate",
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      "page": "circsizer.density",
      "title": "CircSiZer map for density",
      "topics": [
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        "print.circsizer"
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    {
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