{ "_id": "6a211a8bcd65a98ecbd1d31f", "Package": "sysAgNPs", "Title": "Systematic Quantification of AgNPs to Unleash their Potential\nfor Applicability", "Version": "1.0.0", "Authors@R": "c(person(\"Xiting\", \"Wang\", email = \"XitingWang2023@outlook.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0009-0009-5235-0006\")),\nperson(\"Longfei\",\"Mao\",email = \"longfeimao99@gmail.com\",role = c(\"aut\",\"cph\"),comment = c(ORCID = \"0000-0003-0759-0501\")),\nperson(\"Jiamin\",\"Hu\",email = \"jiamin-hu@hotmail.com\",role = c(\"ctb\"), comment = c(ORCID = \"0000-0003-3030-2117\"))\n)", "Description": "There is variation across AgNPs due to differences in\ncharacterization techniques and testing metrics employed in\nstudies. To address this problem, we have developed a\nsystematic evaluation framework called 'sysAgNPs'. Within this\nframework, Distribution Entropy (DE) is utilized to measure the\nuncertainty of feature categories of AgNPs, Proclivity Entropy\n(PE) assesses the preference of these categories, and\nCombination Entropy (CE) quantifies the uncertainty of feature\ncombinations of AgNPs. Additionally, a Markov chain model is\nemployed to examine the relationships among the sub-features of\nAgNPs and to determine a Transition Score (TS) scoring standard\nthat is based on steady-state probabilities. The 'sysAgNPs'\nframework provides metrics for evaluating AgNPs, which helps to\nunravel their complexity and facilitates effective comparisons\namong different AgNPs, thereby advancing the scientific\nresearch and application of these AgNPs.", "License": "GPL (>= 3)", "Language": "en-US", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.2", "LazyData": "true", "URL": "https://github.com/xitingwang-ida/sysAgNPs", "Config/pak/sysreqs": "cmake make libicu-dev libssl-dev libx11-dev\nzlib1g-dev", "Repository": "https://xitingwang-ida.r-universe.dev", "Date/Publication": "2025-03-01 13:24:16 UTC", "RemoteUrl": "https://github.com/xitingwang-ida/sysagnps", "RemoteRef": "HEAD", "RemoteSha": "7390aff7b4c0dc5a1e371ebb51fd0d9345ad941f", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-03 11:06:12 UTC", "User": "root" }, "Author": "Xiting Wang [aut, cre] (ORCID: ),\nLongfei Mao [aut, cph] (ORCID: ),\nJiamin Hu [ctb] (ORCID: )", "Maintainer": "Xiting Wang ", 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147379893 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "dplyr", "role": "Imports" }, { "package": "expm", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "ggpubr", "role": "Imports" }, { "package": "magrittr", "role": "Imports" }, { "package": "patchwork", "role": "Imports" }, { "package": "purrr", "role": "Imports" }, { "package": "rio", "role": "Imports" }, { "package": "tibble", "role": "Imports" }, { "package": "tidyr", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "RColorBrewer", "role": "Imports" }, { "package": "forcats", "role": "Imports" }, { "package": "stats", "role": "Imports" } ], "_owner": "xitingwang-ida", "_selfowned": true, "_usedby": 0, "_updates": [], "_tags": [], "_stars": 0, "_contributors": [ { "user": "xitingwang-ida", "count": 19, "uuid": 147379893 } ], "_userbio": { "uuid": 147379893, "type": "user", "name": "xitingwang-ida" }, 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"Ag precursor", "Reducing agent", "Stabilizing agent", "UV absorption spectra", "Functionalization", "Crystal structure", "Biological assay", "Plants", "Bacteria", "Chemical reduction", "Arc discharge", "Fungi", "Algae", "Electrochemistry", "Photochemistry", "Sonochemistry", "Microwave-assisted", "Laser ablation", "Thermal decomposition", "Ball milling", "Evaporation-condensation", "Spray pyrolysis", "Acidic", "Neutral", "Alkaline", "Medium ((20,50])", "High ((50,Inf))", "Low ((-Inf,20])", "|ζ| < 30", "|ζ| ≥ 30", "(0,20]", "(20,40]", "(40,60]", "(60,80]", "(100,Inf)", "(80,100]", "Spherical", "Irregular", "Nonspherical regular", "Polymorphic", "Antibacterial", "Anticancer", "Antioxidant", "Catalytic", "Antifungal", "Insecticidal", "Electric", "Anticorrosion", "Anti-inflammatory" ], "rows": 600, "table": true, "tojson": true }, { "name": "dataset", "title": "Nanosilver data set.", "object": "dataset", "class": [ "data.frame" ], "fields": [ "Synthesis methods", "Plant parts or microbial 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"Calculate Axis Path. This function is derived from the 'ggradar' package.. Calculates x-y coordinates for a set of radial axes (one per variable being plotted in radar plot)", "topics": [ "sys_CalculateAxisPath" ] }, { "page": "sys_CalculateGroupPath", "title": "Calculate Group Path. This function is derived from the 'ggradar' package.. Converts variable values into a set of radial x-y coordinates", "topics": [ "sys_CalculateGroupPath" ] }, { "page": "sys_CE", "title": "Calculate the Combination Entropy", "topics": [ "sys_CE" ] }, { "page": "sys_DE", "title": "Calculate the Distribution Entropy", "topics": [ "sys_DE" ] }, { "page": "sys_discretize", "title": "Convert categorical variables into discrete variables", "topics": [ "sys_discretize" ] }, { "page": "sys_eval_cri", "title": "Build Transition Scores criteria", "topics": [ "sys_eval_cri" ] }, { "page": "sys_funcCircleCoords", "title": "Generate circle coordinates. This function is derived from the 'ggradar' package.. Generate coordinates to draw a circle.", "topics": [ "sys_funcCircleCoords" ] }, { "page": "sys_generate_color_values", "title": "Generate Dynamic Color Values. This function is derived from the 'ggradar' package.. This function dynamically generates a vector of color values based on the number of groups. It uses RColorBrewer for smaller sets of groups and generates a gradient for larger sets.", "topics": [ "sys_generate_color_values" ] }, { "page": "sys_ggradar", "title": "This function is derived from the 'ggradar' package..", "topics": [ "sys_ggradar" ] }, { "page": "sys_iter", "title": "Obtain the transition probability of each iteration", "topics": [ "sys_iter" ] }, { "page": "sys_line_radar", "title": "Line and Radar Plot of sysAgNPs score", "topics": [ "sys_line_radar" ] }, { "page": "sys_PE", "title": "Calculate the Proclivity Entropy", "topics": [ "sys_PE" ] }, { "page": "sys_steady", "title": "Iterate to obtain the steady state probability", "topics": [ "sys_steady" ] }, { "page": "sys_tran", "title": "Calculate transition probability matrix", "topics": [ "sys_tran" ] }, { "page": "sys_TS", "title": "Calculate the Transition Scores", "topics": [ "sys_TS" ] }, { "page": "sysAgNPs_score", "title": "sysAgNPs package application results in four evaluation scores.", "topics": [ 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