VCA: Variance Component Analysis

ANOVA and REML estimation of linear mixed models is implemented, once following Searle et al. (1991, ANOVA for unbalanced data), once making use of the 'lme4' package. The primary objective of this package is to perform a variance component analysis (VCA) according to CLSI EP05-A3 guideline "Evaluation of Precision of Quantitative Measurement Procedures" (2014). There are plotting methods for visualization of an experimental design, plotting random effects and residuals. For ANOVA type estimation two methods for computing ANOVA mean squares are implemented (SWEEP and quadratic forms). The covariance matrix of variance components can be derived, which is used in estimating confidence intervals. Linear hypotheses of fixed effects and LS means can be computed. LS means can be computed at specific values of covariables and with custom weighting schemes for factor variables. See ?VCA for a more comprehensive description of the features.

Version: 1.5.1
Depends: R (≥ 3.0.0)
Imports: stats, graphics, grDevices, lme4, Matrix, methods, numDeriv
Suggests: VFP, STB, knitr, rmarkdown, prettydoc, RUnit
Published: 2024-03-07
DOI: 10.32614/CRAN.package.VCA
Author: Andre Schuetzenmeister [aut, cre], Florian Dufey [aut]
Maintainer: Andre Schuetzenmeister <andre.schuetzenmeister at>
License: GPL (≥ 3)
NeedsCompilation: yes
CRAN checks: VCA results


Reference manual: VCA.pdf
Vignettes: Performing Variance Component Analyses using R-Package VCA


Package source: VCA_1.5.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): VCA_1.5.1.tgz, r-oldrel (arm64): VCA_1.5.1.tgz, r-release (x86_64): VCA_1.5.1.tgz, r-oldrel (x86_64): VCA_1.5.1.tgz
Old sources: VCA archive

Reverse dependencies:

Reverse imports: mcradds, psychonetrics, STB, VFP


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