{ "_id": "6a1edbb2b401979e7340f8fb", "Package": "VCA", "Version": "1.5.2", "Date": "2025-09-05", "Title": "Variance Component Analysis", "Authors@R": "c(person(\"Andre\", \"Schuetzenmeister\", role = c(\"aut\", \"cre\"),\nemail = \"andre.schuetzenmeister@roche.com\",\ncomment = c(ORCID = \"0000-0002-2964-5502\")),\nperson(\"Florian\", \"Dufey\", role = \"aut\",\nemail = \"florian.dufey@roche.com\",\ncomment = c(ORCID = \"0000-0001-6467-8556\")))", "Description": "ANOVA and REML estimation of linear mixed models is\nimplemented, once following Searle et al. (1991, ANOVA for\nunbalanced data), once making use of the 'lme4' package. The\nprimary objective of this package is to perform a variance\ncomponent analysis (VCA) according to CLSI EP05-A3 guideline\n\"Evaluation of Precision of Quantitative Measurement\nProcedures\" (2014). There are plotting methods for\nvisualization of an experimental design, plotting random\neffects and residuals. For ANOVA type estimation two methods\nfor computing ANOVA mean squares are implemented (SWEEP and\nquadratic forms). The covariance matrix of variance components\ncan be derived, which is used in estimating confidence\nintervals. Linear hypotheses of fixed effects and LS means can\nbe computed. LS means can be computed at specific values of\ncovariables and with custom weighting schemes for factor\nvariables. See ?VCA for a more comprehensive description of the\nfeatures.", "License": "GPL (>= 3)", "VignetteBuilder": "knitr, rmarkdown", "RoxygenNote": "7.2.3", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-20 08:43:08 UTC", "User": "root" }, "Author": "Andre Schuetzenmeister [aut, cre] (ORCID:\n), Florian Dufey [aut]\n(ORCID: )", "Maintainer": "Andre Schuetzenmeister ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2025-09-06 05:12:08 UTC", "RemoteUrl": "https://github.com/cran/VCA", "RemoteRef": "HEAD", "RemoteSha": "59e700c78df15ca2cd63f741a35378961d53ba0a", "MD5sum": "401b249487f4d64da5b3a678f73787de", "_user": "cran", "_type": "src", "_file": "VCA_1.5.2.tar.gz", "_fileid": "e823b30d7b73c10321c9bc36539ffc2ed344c41d2c63822a0f3860ba7787f561", "_filesize": 2401614, "_sha256": "e823b30d7b73c10321c9bc36539ffc2ed344c41d2c63822a0f3860ba7787f561", "_created": "2026-05-20T08:43:08.000Z", "_published": "2026-06-02T13:33:38.539Z", "_distro": "noble", "_jobs": [ { 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"checkData", "title": "Check Random Model for Given Dataset.", "topics": [ "checkData" ] }, { "page": "checkVars", "title": "Check Tow Formula Terms for Potential Problems.", "topics": [ "checkVars" ] }, { "page": "chol2invData", "title": "Dataset Generating Error in Function 'chol2inv'", "topics": [ "chol2invData" ] }, { "page": "coef.VCA", "title": "Extract Fixed Effects from 'VCA' Object", "topics": [ "coef.VCA" ] }, { "page": "combineVC", "title": "Combine Variance Components and Adjust Degrees of Freedom.", "topics": [ "combineVC" ] }, { "page": "dataEP05A2_1", "title": "Simulated Data of a CLSI EP05-A2 20/2/2 Experiment", "topics": [ "dataEP05A2_1" ] }, { "page": "dataEP05A2_2", "title": "Simulated Data of a CLSI EP05-A2 20/2/2 Experiment", "topics": [ "dataEP05A2_2" ] }, { "page": "dataEP05A2_3", "title": "Simulated Data of a CLSI EP05-A2 20/2/2 Experiment", "topics": [ "dataEP05A2_3" ] }, { "page": "dataEP05A3_MS_1", "title": "Simulated Data of a CLSI EP05-A3 3/5/5 Multi-Site Experiment", "topics": [ "dataEP05A3_MS_1" ] }, { "page": "dataEP05A3_MS_2", "title": "Simulated Data of a CLSI EP05-A3 3/5/5 Multi-Site Experiment", "topics": [ "dataEP05A3_MS_2" ] }, { "page": "dataEP05A3_MS_3", "title": "Simulated Data of a CLSI EP05-A3 3/5/5 Multi-Site Experiment", "topics": [ "dataEP05A3_MS_3" ] }, { "page": "dataRS0003_1", "title": "Simulated Repeated Measurements Data.", "topics": [ "dataRS0003_1" ] }, { "page": "dataRS0003_2", "title": "Simulated Repeated Measurements Data.", "topics": [ "dataRS0003_2" ] }, { "page": "dataRS0003_3", "title": "Simulated Repeated Measurements Data.", "topics": [ "dataRS0003_3" ] }, { "page": "dataRS0005_1", "title": "Simulated Data of 5/3 Experiment.", "topics": [ "dataRS0005_1" ] }, { "page": "dataRS0005_2", "title": "Simulated Data of 5/3 Experiment.", "topics": [ "dataRS0005_2" ] }, { "page": "dataRS0005_3", "title": "Simulated Data of 5/3 Experiment.", "topics": [ "dataRS0005_3" ] }, { "page": "DfSattHelper", "title": 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"topics": [ "getDDFM" ] }, { "page": "getDF", "title": "Extract Degrees of Freedom from Linear Hypotheses of Fixed Effects or LS Means", "topics": [ "getDF" ] }, { "page": "getGB", "title": "Giesbrecht & Burns Approximation of the Variance-Covariance Matrix of Variance Components", "topics": [ "getGB" ] }, { "page": "getIP.remlVCA", "title": "Intermediate Precision for remlVCA-fitted objects of class 'VCA'", "topics": [ "getIP.remlVCA" ] }, { "page": "getL", "title": "Construct Linear Contrast Matrix for Hypothesis Tests", "topics": [ "getL" ] }, { "page": "getMat", "title": "Extract a Specific Matrix from a 'VCA' Object", "topics": [ "getMat" ] }, { "page": "getMM", "title": "Overparameterized Design Matrices", "topics": [ "getMM" ] }, { "page": "getSSQsweep", "title": "ANOVA Sum of Squares via Sweeping", "topics": [ "getSSQsweep" ] }, { "page": "getV", "title": "Determine V-Matrix for a 'VCA' Object", "topics": [ "getV" ] }, { "page": "Glucose", "title": "Inermediate Precision Data from CLSI EP05-A3", "topics": [ "Glucose" ] }, { "page": "HugeData", "title": "Huge Dataset with Three Variables", "topics": [ "HugeData" ] }, { "page": "isBalanced", "title": "Check Whether Design Is Balanced Or Not", "topics": [ "isBalanced" ] }, { "page": "legend.m", "title": "Add Legend to Margin.", "topics": [ "legend.m" ] }, { "page": "lmerG", "title": "Construct Variance-Covariance Matrix of Random Effects for Models Fitted by Function 'lmer'", "topics": [ "lmerG" ] }, { "page": "lmerMatrices", "title": "Derive and Compute Matrices for Objects Fitted by Function 'lmer'", "topics": [ "lmerMatrices" ] }, { "page": "lmerSummary", "title": "Derive VCA-Summary Table from an Object Fitted via Function 'lmer'", "topics": [ "lmerSummary" ] }, { "page": "load_if_installed", "title": "Load 'RevoUtilsMath'-package if available", "topics": [ "load_if_installed" ] }, { "page": "lsmeans", "title": "Least Squares Means of Fixed Effects", "topics": [ "lsmeans" ] }, { "page": "LSMeans_Data", "title": "Dataset for Unit-Testing of LS Means", "topics": [ "LSMeans_Data" ] }, { "page": "lsmMat", "title": "Contrast Matrix for LS Means", "topics": [ "lsmMat" ] }, { "page": "MLrepro", "title": "Multi-Lot Reproducibility Data.", "topics": [ "MLrepro" ] }, { "page": "model.frame.VCA", "title": "Extract the Model Frame from a 'VCA' Object", "topics": [ "model.frame.VCA" ] }, { "page": "model.matrix.VCA", "title": "Model Matrix of a Fitted VCA-Object", "topics": [ "model.matrix.VCA" ] }, { "page": "MPinv", "title": "Moore-Penrose Generalized Inverse of a Matrix", "topics": [ "MPinv" ] }, { "page": "orderData", "title": "Re-Order Data.Frame", "topics": [ "orderData" ] }, { "page": "Orthodont", "title": "Orthodont dataset from R-package 'nlme'", "topics": [ "Orthodont" ] }, { "page": "plot.VCA", "title": "Standard 'plot' Method for 'VCA' S3-Objects.", "topics": [ "plot.VCA" ] }, { "page": "plotRandVar", "title": "Plot Random Variates of a Mixed Model ('VCA' Object).", "topics": [ "plotRandVar" ] }, { "page": "predict.VCA", "title": "Predictions from a Model Fitted by 'fitLMM'", "topics": [ "predict.VCA" ] }, { "page": "print.VCA", "title": "Standard Printing Method for Objects of Class 'VCA'", "topics": [ "print.VCA" ] }, { "page": "print.VCAinference", "title": "Standard Print Method for Objects of Class 'VCAinference'", "topics": [ "print.VCAinference" ] }, { "page": "protectedCall", "title": "Wrap Function-Calls to Execute Additional Checks.", "topics": [ "protectedCall" ] }, { "page": "ranef", "title": "Generic Method for Extracting Random Effects from a Fitted Model", "topics": [ "ranef" ] }, { "page": "ranef.VCA", "title": "Extract Random Effects from 'VCA' Object", "topics": [ "ranef.VCA" ] }, { "page": "realData", "title": "Real-World Data", "topics": [ "realData" ] }, { "page": "remlMM", "title": "Fit Linear Mixed Models via REML", "topics": [ "remlMM" ] }, { "page": "remlVCA", "title": "Perform (V)ariance (C)omponent (A)nalysis via REML-Estimation", 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Cholesky-Root", "topics": [ "Solve" ] }, { "page": "solveMME", "title": "Solve Mixed Model Equations", "topics": [ "solveMME" ] }, { "page": "stepwiseVCA", "title": "Bottom-Up Step-Wise VCA-Analysis of the Complete Dataset", "topics": [ "stepwiseVCA" ] }, { "page": "summarize.VCA", "title": "Summarize Outcome of a Variance Component Analysis.", "topics": [ "summarize.VCA", "summarize.VCAinference" ] }, { "page": "test.fixef", "title": "Perform t-Tests for Linear Contrasts on Fixed Effects", "topics": [ "test.fixef" ] }, { "page": "test.lsmeans", "title": "Perform t-Tests for Linear Contrasts on LS Means", "topics": [ "test.lsmeans" ] }, { "page": "Trace", "title": "Compute the Trace of a Matrix", "topics": [ "Trace" ] }, { "page": "varPlot", "title": "Variability Chart for Hierarchical Models.", "topics": [ "varPlot" ] }, { "page": "VCAdata1", "title": "Simulated Data for Variance Component Analysis.", "topics": [ "VCAdata1" ] }, { "page": "VCAinference", "title": "Inferential 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