Time series decomposition for univariate time series using the
    "Verallgemeinerte Berliner Verfahren" (Generalized Berlin Method) as
    described in 'Kontinuierliche Messgrößen und Stichprobenstrategien in
    Raum und Zeit mit Anwendungen in den Natur-, Umwelt-, Wirtschafts-
    und Finanzwissenschaften', by
    Hebbel and Steuer, Springer Berlin Heidelberg, 2022
    <doi:10.1007/978-3-662-65638-9>, or
    'Decomposition of Time Series using the Generalised Berlin 
    Method (VBV)' by Hebbel and Steuer, in Jan Beran, Yuanhua Feng, Hartmut
    Hebbel (Eds.): Empirical Economic and Financial Research - Theory,
    Methods and Practice, Festschrift in Honour of Prof. Siegfried Heiler.
    Series: Advanced Studies in Theoretical and Applied Econometrics.
    Springer 2014, p. 9-40.

Provides new classes for (rotated) BB1, BB6, BB7, BB8, and 
  Tawn copulas, extends the existing Gumbel and Clayton families with 
  rotations, and allows to set up a vine copula model using the 'copula' API.
  Corresponding objects from the 'VineCopula' API can easily be converted.

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.