{ "_id": "6a10517bacfb0bcc41ca1424", "Package": "clordr", "Type": "Package", "Title": "Composite Likelihood Inference and Diagnostics for Replicated\nSpatial Ordinal Data", "Version": "1.7.2", "Authors@R": "c(person(\"Ting Fung\", \"Ma\", email = \"tingfung@mailbox.sc.edu\", role = c(\"cre\", \"aut\")),\nperson(\"Pingping\", \"Wang\", email = \"catherinewpp@126.com\", role = \"aut\"),\nperson(\"Jun\", \"Zhu\", email = \"jzhu@stat.wisc.edu\", role = \"aut\"),\nperson(\"Dipankar\", \"Bandyopadhyay\", email = \"bandyopd@gmail.com\", role = \"ctb\"),\nperson(\"Yincai\", \"Tang\", email = \"yctang@stat.ecnu.edu.cn\", role= \"ctb\"))", "Maintainer": "Ting Fung Ma ", "Description": "Composite likelihood parameter estimate and asymptotic\ncovariance matrix are calculated for the spatial ordinal data\nwith replications, where spatial ordinal response with\ncovariate and both spatial exponential covariance within\nsubject and independent and identically distributed measurement\nerror. Parameter estimation can be performed by either solving\nthe gradient function or maximizing composite log-likelihood.\nParametric bootstrapping is used to estimate the Godambe\ninformation matrix and hence the asymptotic standard error and\ncovariance matrix with parallel processing option. Moreover,\nthe proposed surrogate residual, which extends the results of\nLiu and Zhang (2017) , can\nact as a useful tool for model diagnostics.", "License": "GPL-2", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-09 18:59:14 UTC", "User": "root" }, "Author": "Ting Fung Ma [cre, aut], Pingping Wang [aut], Jun Zhu [aut],\nDipankar Bandyopadhyay [ctb], Yincai Tang [ctb]", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-05-09 18:09:55 UTC", "RemoteUrl": "https://github.com/cran/clordr", "RemoteRef": "HEAD", "RemoteSha": "b91bfad2bcae0d392f8573f640c9e15d624d4b31", "MD5sum": "20adc164560e91b8b1f820ba5f054110", "_user": "cran", "_type": "src", "_file": "clordr_1.7.2.tar.gz", "_fileid": "0c068139ee1a289955abab6e81928056206bc873b389f12a74a91953cea4ea73", "_filesize": 163194, "_sha256": "0c068139ee1a289955abab6e81928056206bc873b389f12a74a91953cea4ea73", "_created": "2026-05-09T18:59:14.000Z", "_published": 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