{ "_id": "6a1eedd9b401979e73412872", "Package": "bayespm", "Version": "0.2.0", "Title": "Bayesian Statistical Process Monitoring", "Authors@R": "c(person(\"Dimitrios\", \"Kiagias\", \nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"kiagias.dim@gmail.com\"),\nperson(\"Konstantinos\", \"Bourazas\",\nrole = c(\"aut\", \"cph\"),\nemail = \"bourazas.konstantinos@ucy.ac.cy\"),\nperson(\"Panagiotis\", \"Tsiamyrtzis\",\nrole = c(\"aut\", \"cph\"),\nemail = \"panagiotis.tsiamyrtzis@polimi.it\"))", "Author": "Dimitrios Kiagias [aut, cre, cph], Konstantinos Bourazas [aut,\ncph], Panagiotis Tsiamyrtzis [aut, cph]", "Maintainer": "Dimitrios Kiagias ", "Description": "The R-package bayespm implements Bayesian Statistical\nProcess Control and Monitoring (SPC/M) methodology. These\nmethods utilize available prior information and/or historical\ndata, providing efficient online quality monitoring of a\nprocess, in terms of identifying moderate/large transient\nshifts (i.e., outliers) or persistent shifts of medium/small\nsize in the process. These self-starting, sequentially updated\ntools can also run under complete absence of any prior\ninformation. The Predictive Control Charts (PCC) are introduced\nfor the quality monitoring of data from any discrete or\ncontinuous distribution that is a member of the regular\nexponential family. The Predictive Ratio CUSUMs (PRC) are\nintroduced for the Binomial, Poisson and Normal data (a later\nversion of the library will cover all the remaining\ndistributions from the regular exponential family). The PCC\ntargets transient process shifts of typically large size\n(a.k.a. outliers), while PRC is focused in detecting persistent\n(structural) shifts that might be of medium or even small size.\nApart from monitoring, both PCC and PRC provide the\nsequentially updated posterior inference for the monitored\nparameter. Bourazas K., Kiagias D. and Tsiamyrtzis P. (2022)\n\"Predictive Control Charts (PCC): A Bayesian approach in online\nmonitoring of short runs\" ,\nBourazas K., Sobas F. and Tsiamyrtzis, P. 2023. \"Predictive\nratio CUSUM (PRC): A Bayesian approach in online change point\ndetection of short runs\" ,\nBourazas K., Sobas F. and Tsiamyrtzis, P. 2023. \"Design and\nproperties of the predictive ratio cusum (PRC) control charts\"\n.", "License": "GPL (>= 2)", "LazyData": "true", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-19 08:58:34 UTC", "User": "root" }, "Repository": "https://kiagiasdim.r-universe.dev", "Date/Publication": "2023-09-10 23:30:44 UTC", "RemoteUrl": "https://github.com/cran/bayespm", "RemoteRef": "HEAD", "RemoteSha": "c021e6605f3bf1ac4e6c5f5141e42dd456e0b8a6", "MD5sum": "174fab898808ad1c633229a4f7845cb0", "_user": "kiagiasdim", "_type": "src", "_file": "bayespm_0.2.0.tar.gz", "_fileid": "4b7f833acf5db388cd6a980eea59e8a75268f51fee41f93f22ab099fa249058f", "_filesize": 269298, "_sha256": 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