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 EP05A3 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.
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 Package
 VCA
 Version
 1.4.0
 Date
 20190710
 Title
 Variance Component Analysis
 Author
 Andre Schuetzenmeister [aut, cre], Florian Dufey [aut]
 Maintainer
 Andre Schuetzenmeister
 Depends
 R (>= 3.0.0)
 Imports
 stats, graphics, grDevices, lme4, Matrix, methods, numDeriv
 Suggests
 VFP, STB
 Description

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 EP05A3 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.
 License
 GPL (>= 3)
 RoxygenNote
 6.0.1
 NeedsCompilation
 yes
 Packaged
 20190710 16:00:34 UTC; schueta6
 Repository
 CRAN
 Date/Publication
 20190710 16:32:42 UTC