{ "_id": "6a1034e4acfb0bcc41c99878", "Package": "PhysioIndexR", "Title": "Physiological and Stress Indices for Crop Evaluation", "Version": "0.1.0", "Authors@R": "c(person(family = \"Vinayaka\",\nrole = c(\"aut\", \"cre\"),\nemail = \"vinayaka.b3vs@gmail.com\",\ncomment = c(ORCID = \"0000-0001-5004-0084\")),\nperson(given = \"Vengavasi\",\nfamily = \"Krishnapriya\",\nrole = c(\"aut\", \"ctb\"),\nemail = \"krishnapriya19@gmail.com\",\ncomment = c(ORCID = \"0000-0002-7496-5302\")),\nperson(given = \"T.\",\nfamily = \"Lakshmi Pathy\",\nrole = c(\"aut\", \"ctb\"),\nemail = \"pathy4u76@gmail.com\",\ncomment = c(ORCID = \"0000-0001-8940-7971\")),\nperson(family = \"Amaresh\",\nrole = c(\"aut\", \"ctb\"),\nemail = \"amaresh541@gmail.com\",\ncomment = c(ORCID = \"0009-0000-5201-5755\")),\nperson(given = \"K.\",\nfamily = \"Gopalareddy\",\nrole = c(\"aut\", \"ctb\"),\nemail = \"gopalgpb@gmail.com\",\ncomment = c(ORCID = \"0000-0002-8825-6363\")),\nperson(given = \"G.S.\",\nfamily = \"Suresha\",\nrole = c(\"aut\", \"ctb\"),\nemail = \"sureshgiriyapur@gmail.com\",\ncomment = c(ORCID = \"0000-0002-3283-6617\")),\nperson(given = \"P.\",\nfamily = \"Govindaraj\",\nrole = c(\"aut\", \"ctb\")))", "Maintainer": "Vinayaka ", "Description": "Crop production systems are increasingly challenged by\nclimate variability, resource limitations, and biotic–abiotic\nstresses. In this context, stress tolerance indices and\nphysiological trait estimators are essential tools to identify\nstable and superior genotypes, quantify yield stability under\nstress versus non-stress conditions, and understand plant\nadaptive responses. The 'PhysioIndexR' package provides a\nunified framework to compute commonly used stress indices,\nphysiological traits, and derived metrics that are critical in\ncrop improvement, crop physiology, and other agricultural\nsciences. The package includes functions to calculate classical\nstress tolerance indices (See Lamba et al., 2023;\n) such as Tolerance (TOL),\nStress Tolerance Index (STI), Stress Susceptibility Percentage\nIndex (SSPI), Yield Index (YI), Yield Stability Index (YSI),\nRelative Stress Index (RSI), Mean Productivity (MP), Geometric\nMean Productivity (GMP), Harmonic Mean (HM), Mean Relative\nPerformance (MRP), and Percent Yield Reduction (PYR), along\nwith a convenience wrapper all_indices() that returns all\nindices simultaneously. The function mfvst_from_indices()\nintegrates these indices into a composite stress score using\ndirection-aware membership values (0–1 scaling) and also\naveraging, facilitating genotype ranking and selection (See\nVinu et al., 2025; ). The\npackage also implements two novel composite functions:\nWMFVST(), which computes the Weighted Mean Membership Function\nValue for Stress Tolerance, and WASI(), which computes the\nWeighted Average Stress Index, both derived from membership\nfunction values (MFV) and raw stress index values,\nrespectively. Beyond stress indices, the package provides\nfunctions for key physiological traits relevant to sugarcane\nand other crops: bmap() computes biomass accumulation and\npartitioning between leaf, cane/shoot, and root fractions.\nchl() estimates total chlorophyll content from Soil-Plant\nAnalysis Development (SPAD) and Chlorophyll Content Index (CCI)\nvalues using validated quadratic models particularly for\nsugarcane (See Krishnapriya et al., 2020;\n). ctd() calculates canopy\ntemperature depression (CTD) from ambient and canopy\ntemperatures, an important indicator of transpiration\nefficiency. growth() computes key growth analysis parameters,\nincluding Leaf Area Index (LAI), Net Assimilation Rate (NAR),\nand Crop Growth Rate (CGR) across crop growth stages (See\nWatson, 1958; ).\nranking() provides flexible ranking utilities for genotype\nperformance with multiple tie-handling and NA-placement\noptions. Through these tools, the package enables researchers\nto: (i) quantify crop responses to stress environments, (ii)\npartition physiological components of yield, (iii) integrate\nmultiple indices into composite metrics for genotype\nevaluation, and (iv) facilitate informed decision making in\nbreeding pipelines, and plant physiology experiments. By\ncombining physiology-based traits with quantitative stress\nindices, 'PhysioIndexR' supports comprehensive crop evaluation\nand helps researchers identify multi-stress-resilient superior\ngenotypes, thereby contributing to genetic improvement and\nensuring sustainable production of food, fuel, and fibre in the\nera of limited resources and climate change.", "License": "GPL-3", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-12 07:31:32 UTC", "User": "root" }, "Author": "Vinayaka [aut, cre] (ORCID:\n), Vengavasi\nKrishnapriya [aut, ctb] (ORCID:\n), T. Lakshmi Pathy\n[aut, ctb] (ORCID: ),\nAmaresh [aut, ctb] (ORCID:\n), K. Gopalareddy [aut,\nctb] (ORCID: ), G.S.\nSuresha [aut, ctb] (ORCID:\n), P. Govindaraj [aut,\nctb]", "Repository": "https://vinayaka11442.r-universe.dev", "Date/Publication": "2025-11-12 10:50:01 UTC", "RemoteUrl": "https://github.com/cran/PhysioIndexR", "RemoteRef": "HEAD", "RemoteSha": "b96a23ba5ef8d8c5946423a429d8f65acc278841", "MD5sum": "9a651b45d78c0ab77d1cdfec41d3a770", "_user": "vinayaka11442", "_type": "src", "_file": "PhysioIndexR_0.1.0.tar.gz", "_fileid": "21655d244b1770d68c39ed477e8e3f95b6ce0d2471490dc01ce18ac74688bb6d", "_filesize": 218131, "_sha256": "21655d244b1770d68c39ed477e8e3f95b6ce0d2471490dc01ce18ac74688bb6d", "_created": "2026-05-12T07:31:32.000Z", "_published": "2026-05-22T10:50:12.493Z", "_distro": "noble", "_jobs": [ { "job": 77364951463, "time": 92, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "6938247644" }, { "job": 77364951673, "time": 113, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "6938253737" }, { "job": 77364951670, "time": 96, "config": "macos-oldrel-arm64", "r": 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