{ "_id": "6a1570dcacfb0bcc41d64f74", "Package": "mvMonitoring", "Type": "Package", "Title": "Multi-State Adaptive Dynamic Principal Component Analysis for\nMultivariate Process Monitoring", "Version": "0.2.4", "Date": "2023-11-21", "Authors@R": "c(\nperson(\n\"Melissa\", \"Innerst\", email = \"innerst@juniata.edu\", role = c(\"aut\")\n),\nperson(\n\"Gabriel\", \"Odom\", email = \"gabriel.odom@fiu.edu\", role = c(\"aut\", \"cre\")\n),\nperson(\n\"Ben\", \"Barnard\", email = \"ben_barnard@baylor.edu\", role = c(\"aut\")\n),\nperson(\"Karen\", \"Kazor\", role = c(\"aut\")),\nperson(\n\"Amanda\", \"Hering\", email = \"mandy_hering@baylor.edu\", role = c(\"aut\")\n)\n)", "Description": "Use multi-state splitting to apply Adaptive-Dynamic PCA\n(ADPCA) to data generated from a continuous-time multivariate\nindustrial or natural process. Employ PCA-based dimension\nreduction to extract linear combinations of relevant features,\nreducing computational burdens. For a description of ADPCA, see\n, the 2016 paper from Kazor et\nal. The multi-state application of ADPCA is from a manuscript\nunder current revision entitled \"Multi-State Multivariate\nStatistical Process Control\" by Odom, Newhart, Cath, and\nHering, and is expected to appear in Q1 of 2018.", "License": "GPL-2", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.2.3", "VignetteBuilder": "knitr", "URL": "https://github.com/gabrielodom/mvMonitoring", "Config/testthat/edition": "3", "Repository": "https://gabrielodom.r-universe.dev", "Date/Publication": "2023-11-21 17:10:29 UTC", "RemoteUrl": "https://github.com/gabrielodom/mvmonitoring", "RemoteRef": "HEAD", "RemoteSha": "1b081c8866a963f69c530487901e13aca3623034", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-26 10:04:09 UTC", "User": "root" }, "Author": "Melissa Innerst [aut],\nGabriel Odom [aut, cre],\nBen Barnard [aut],\nKaren Kazor [aut],\nAmanda Hering [aut]", "Maintainer": "Gabriel Odom ", "MD5sum": "5297f72b43c974d4bfaa8a6f7a0203d8", "_user": "gabrielodom", "_type": "src", "_file": "mvMonitoring_0.2.4.tar.gz", "_fileid": "fc193227801e7113643a00a047cd80127034a63ba44360e0edc5f804b54b396b", "_filesize": 3435925, "_sha256": "fc193227801e7113643a00a047cd80127034a63ba44360e0edc5f804b54b396b", "_created": "2026-05-26T10:04:09.000Z", "_published": "2026-05-26T10:07:24.529Z", "_distro": "noble", "_jobs": [ { "job": 77851698206, "time": 138, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7214291471" }, { "job": 77851698277, "time": 133, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7214289509" }, { "job": 77851698300, "time": 152, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7214285675" }, { "job": 77851698224, "time": 128, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7214281064" }, { "job": 77851286088, "time": 152, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7214242520" }, { "job": 77851698221, "time": 104, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7214279153" }, { "job": 77851698236, "time": 115, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7214283340" }, { "job": 77851698260, "time": 94, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7214275870" }, { "job": 77851698282, "time": 118, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7214285886" } ], "_buildurl": "https://github.com/r-universe/gabrielodom/actions/runs/26445662295", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/gabrielodom/mvmonitoring", "_commit": { "id": "1b081c8866a963f69c530487901e13aca3623034", "author": "gabrielodom ", "committer": "gabrielodom ", "message": "Per Issue #31\n", "time": 1700586629 }, "_maintainer": { "name": "Gabriel Odom", "email": "gabriel.odom@fiu.edu", "login": "gabrielodom", "twitter": "@RevDocGabriel", "description": "PhD statistician. 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"filename": "MVSPC-Workflow.html", "title": "Multivariate Statistical Process Control with mvMonitoring", "author": "Gabriel Odom", "engine": "knitr::rmarkdown", "headings": [ "Introduction \\label", "Motivation \\label", "Why use mvMonitoring", "Multi- or Single-State AD-PCA", "The msp Functions", "Simulating Data with mspProcessData", "Latent Feature Creation", "Autocorrelated and Non-Stationary Error", "The Non-Linear Latent Process", "Single-State and Multi-State Features", "Single State Features", "Multi-State Features", "Synthetic Fault Induction", "Putting it all together...", "Training with mspTrain", "The xts Data Matrix", "The Class Vector", "Adaptive and Dynamic Modelling", "Model Training", "Monitoring Incoming Observations with mspMonitor", "Issue a Warning with mspWarning", "Example Simulation Workflow", "Generating Synthetic Data", "Train the Fault Detection Threshold", "Test New Observations for Anomalies", "Adding Lagged Features", "Monitoring the Test Data", "Warn 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