Imports: stats, graphics, grDevices, Matrix, methods, numDeriv
Description: ANOVA-type estimation (prediction) of random effects in linear mixed models is implemented, following
Searle et al. 1991 (ANOVA for unbalanced data). For better performance the SWEEP-Operator is now implemented
for generating the ANOVA Type-1 error sum of squares. The primary objective of this package is to perform
Variance Component Analyses (VCA). This is a special type of analysis frequently used in verifying the
The Satterthwaite approximation of the total degrees of freedom and for individual variance components is
implemented. These are used in the Chi-Squared tests against a claimed value, and in the respective confidence intervals.
Satterthwaite's approximation of denominator degrees of freedom in t-/F-tests of fixed effects are also
There are several functions for extracting, random effects, fixed effects, variance-covariance
matrices of random and fixed effects. Residuals (marginal, conditional) can be extracted as raw, standardized
Additionally, plotting methods for residuals and random effects and a variability chart are available. The latter
is useful for visualizing the variability in sub-classes emerging from the experimental design.

Imports: stats, graphics, grDevices, lme4, Matrix, methods, numDeriv
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 a perform 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.