{ "_id": "6abf4d4e99f7aa8c6aa5890f", "Package": "tiltdens", "Type": "Package", "Title": "Tilted and Data-Sharpened Nonparametric Density Estimation", "Version": "0.2.1", "Authors@R": "person(\"Hassan\", \"Doosti\", email = \"hassan.doosti@mq.edu.au\",\nrole = c(\"aut\", \"cre\", \"cph\"))", "Description": "High-order nonparametric density estimators built by\nperturbing a conventional kernel estimator, either by\nre-weighting the observations (\"tilting\") or by moving them\n(\"data sharpening\"). The perturbation is chosen so that the\nestimator inherits the fast convergence rate of an\ninfinite-order kernel estimator, such as the sinc or\ntrapezoidal flat-top estimator, while remaining a proper\nnon-negative density without the oscillatory tails those\nestimators suffer from. Two criteria are provided: minimising\nthe L2 distance to an infinite-order comparator, following\nDoosti and Hall (2016) , and minimising\na cross-validation criterion that needs no comparator and is\nmuch faster, following Doosti, Hall and Mateu (2018)\n.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Language": "en-GB", "VignetteBuilder": "knitr", "RoxygenNote": "7.3.1", "Config/testthat/edition": "3", "URL": "https://github.com/DoostiH/tiltdens", "BugReports": "https://github.com/DoostiH/tiltdens/issues", "Repository": "https://doostih.r-universe.dev", "Date/Publication": "2026-10-02 02:08:10 UTC", "RemoteUrl": "https://github.com/doostih/tiltdens", "RemoteRef": "HEAD", "RemoteSha": "bc1cc9d1e91bc2cd9e0862632865d5201bf865d3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-02 06:09:53 UTC", "User": "root" }, "Author": "Hassan Doosti [aut, cre, cph]", "Maintainer": "Hassan Doosti ", "_user": "doostih", "_type": "src", "_file": "tiltdens_0.2.1.tar.gz", "_fileid": 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