{ "_id": "6a1f38a2b401979e73428dfe", "Type": "Package", "Package": "Pmetrics", "Title": "Pmetrics for Population Modeling and Simulation", "Version": "3.1.3", "Authors@R": "c(\nperson(\"Michael\", \"Neely\", , \"mneely@usc.edu\", role = c(\"aut\", \"cre\")),\nperson(\"Julián\", \"Otálvaro\", , \"juliandavid347@gmail.com\", role = \"aut\"),\nperson(\"Markus\", \"Hovd\", role = \"aut\"),\nperson(\"Walter\", \"Yamada\", role = \"ctb\"),\nperson(\"Alan\", \"Schumitzky\", role = \"ctb\"),\nperson(\"Rong\", \"Chen\", role = \"ctb\"),\nperson(\"Michael\", \"van Guilder\", role = \"ctb\"),\nperson(\"David\", \"Bayard\", role = \"ctb\"),\nperson(\"Robert\", \"Leary\", role = \"ctb\")\n)", "Description": "Pmetrics is short for pharmacometrics, the discipline of\nmodeling drug kinetics and effects on biologic systems,\nincluding disease progression. Models can be used to simulate\nnovel therapeutic dosing regimens and/or populations, informing\ntrial design and dosing strategies. The package supports\nprimarily non-parametric but also parametric pharmacometric\nmodeling and simulation with functions to run and analyze the\noutput from all three components of the Pmetrics software suite\nfor population pharmacometric data analysis: 1) IT2B (Iterative\nTwo-Stage Bayesian) for parametric models; 2) NPAG\n(Non-parametric Adaptive Grid) for non-paramametric models; 3)\nSimulator for semi-parametric Monte-Carlo simulations.", "License": "GPL (>= 3)", "URL": "https://lapkb.github.io/Pmetrics/", "BugReports": "https://github.com/LAPKB/Pmetrics/issues", "VignetteBuilder": "knitr", "Config/Needs/website": "r-lib/pkgdown, quarto", "Config/rextendr/version": "0.4.2", "Config/roxygen2/version": "8.0.0", "Config/testthat/edition": "3", "Encoding": "UTF-8", "LazyData": "true", "LazyDataCompression": "xz", "Roxygen": "list(markdown = TRUE)", "SystemRequirements": "Cargo (>= 1.82) (Rust's package manager), rustc", "Config/pak/sysreqs": "libcairo2-dev cmake 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