{ "_id": "6a17e064acfb0bcc41d9eaed", "Package": "PhaseGMM", "Type": "Package", "Title": "Phase-Function Based Estimation and Inference for Linear\nErrors-in-Variables (EIV) Models", "Version": "0.1.1", "Authors@R": "c(\nperson(\n\"Chang\", \"Liu\",\nemail = \"leo12345liu@gmail.com\",\nrole = c(\"aut\", \"cre\")\n), person(\n\"Linh\", \"Nghiem\",\nemail = \"l.nghiemum@gmail.com\",\nrole = c(\"aut\")\n)\n)", "Description": "Estimation and inference for coefficients of linear EIV\nmodels with symmetric measurement errors. The measurement\nerrors can be homoscedastic or heteroscedastic, for the latter,\nreplication for at least some observations needs to be\navailable. The estimation method and asymptotic inference are\nbased on a generalised method of moments framework, where the\nestimating equations are formed from (1) minimising the\ndistance between the empirical phase function (normalised\ncharacteristic function) of the response and that of the linear\ncombination of all the covariates at the estimates, and (2)\nminimising a corrected least-square discrepancy function.\nSpecifically, for a linear EIV model with p error-prone and q\nerror-free covariates, if replicates are available, the GMM\napproach is based on a 2(p+q) estimating equations if some\nreplicates are available and based on p+2q estimating equations\nif no replicate is available. The details of the method are\ndescribed in Nghiem and Potgieter (2020)\n and Nghiem and Potgieter (2025)\n.", "License": "GPL-2", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.2", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-28 06:25:44 UTC", "User": "root" }, "Author": "Chang Liu [aut, cre], Linh Nghiem [aut]", "Maintainer": "Chang Liu ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-04-28 17:06:14 UTC", "RemoteUrl": "https://github.com/cran/PhaseGMM", "RemoteRef": "HEAD", "RemoteSha": "6ce11b7336eb4b7b653bbf34f0036cd19f2c895d", "MD5sum": "9e9c319eec60604842e5772be154ca62", "_user": "cran", "_type": "src", "_file": "PhaseGMM_0.1.1.tar.gz", "_fileid": "76510b425e4ee2529d2bf8493037570cdaade6017dc9df7e06d6ae1029b57bfd", "_filesize": 164238, "_sha256": "76510b425e4ee2529d2bf8493037570cdaade6017dc9df7e06d6ae1029b57bfd", "_created": "2026-05-28T06:25:44.000Z", "_published": "2026-05-28T06:27:48.728Z", "_distro": "noble", "_jobs": [ { 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