{ "_id": "6ad73a090d12c08e591b88b4", "Package": "RHawkes", "Type": "Package", "Title": "Renewal Hawkes Process", "Version": "1.0", "Date": "2022-5-4", "Authors@R": "c(person(given=\"Feng\", \nfamily=\"Chen\",\nrole = c(\"aut\", \"cre\"),\nemail = \"feng.chen@unsw.edu.au\",\ncomment = c(ORCID=\"0000-0002-9646-3338\")\n),\nperson(given=\"Tom\",\nfamily=\"Stindl\",\nrole = \"ctb\",\nemail=\"t.stindl@unsw.edu.au\",\ncomment=c(ORCID=\"0000-0001-8627-9337\")\n)\n)", "Description": "The renewal Hawkes (RHawkes) process (Wheatley, Filimonov,\nand Sornette, 2016 ) is an\nextension to the classical Hawkes self-exciting point process\nwidely used in the modelling of clustered event sequence data.\nThis package provides functions to simulate the RHawkes process\nwith a given immigrant hazard rate function and offspring birth\ntime density function, to compute the exact likelihood of a\nRHawkes process using the recursive algorithm proposed by Chen\nand Stindl (2018) , to\ncompute the Rosenblatt residuals for goodness-of-fit\nassessment, and to predict future event times based on observed\nevent times up to a given time. A function implementing the\nlinear time RHawkes process likelihood approximation algorithm\nproposed in Stindl and Chen (2021)\n is also included.", "License": "GPL (>= 2)", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-20 09:50:08 UTC", "User": "root" }, "Author": "Feng Chen [aut, cre] (),\nTom Stindl [ctb] ()", "Maintainer": "Feng Chen ", "Repository": "https://fchenunswms.r-universe.dev", "Date/Publication": "2022-05-05 13:40:09 UTC", "RemoteUrl": "https://github.com/cran/RHawkes", "RemoteRef": "HEAD", "RemoteSha": "9a033bc7a3c829df6aa494c2bf79501c7152df3d", "_user": "fchenunswms", "_type": "src", "_file": "RHawkes_1.0.tar.gz", "_fileid": "https://r2.ropensci.org/b3660aeb2f46833b7b89c2f41dfffc63a3c463f455169a48fede57069dec9ca4", "_filesize": 1079157, "_sha256": "b3660aeb2f46833b7b89c2f41dfffc63a3c463f455169a48fede57069dec9ca4", "_expires": "2027-01-28T09:53:11.000Z", "_created": "2026-10-20T09:50:08.000Z", "_published": "2026-10-20T09:53:13.480Z", "_upstream": 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