{ "_id": "6a1020efacfb0bcc41c8bd9b", "Package": "SNSchart", "Type": "Package", "Title": "Sequential Normal Scores in Statistical Process Management", "Version": "1.4.0", "Authors@R": "c(person(\"Victor\", \"Tercero\", email = \"victor.tercero@tec.mx\", role=c(\"aut\")),\nperson(\"Luis\", \"Benavides\", email = \"luisbv@tec.mx\", role = c(\"aut\", \"cre\")),\nperson(\"Jorge\", \"Merlo\", email = \"jorgemerlo24@gmail.com\", role = c(\"ctb\")))", "Author": "Victor Tercero [aut], Luis Benavides [aut, cre], Jorge Merlo\n[ctb]", "Maintainer": "Luis Benavides ", "Description": "The methods discussed in this package are new\nnon-parametric methods based on sequential normal scores 'SNS'\n(Conover et al (2017) ),\ndesigned for sequences of observations, usually time series\ndata, which may occur singly or in batches, and may be\nunivariate or multivariate. These methods are designed to\ndetect changes in the process, which may occur as changes in\nlocation (mean or median), changes in scale (standard\ndeviation, or variance), or other changes of interest in the\ndistribution of the observations, over the time observed. They\nusually apply to large data sets, so computations need to be\nsimple enough to be done in a reasonable time on a computer,\nand easily updated as each new observation (or batch of\nobservations) becomes available. 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