fdANOVA: an R software package for analysis of variance for univariate and multivariate functional data. Functional data, i.e., observations represented by curves or functions, frequently arise in various fields. The theory and practice of statistical methods for such data is referred to as functional data analysis (FDA) which is one of major research fields in statistics. The practical use of FDA methods is made possible thanks to availability of specialized and usually free software. In particular, a number of R packages is devoted to these methods. In the paper, we introduce a new R package fdANOVA which provides an access to a broad range of global analysis of variance methods for univariate and multivariate functional data. The implemented testing procedures mainly for homoscedastic case are briefly overviewed and illustrated by examples on a well known functional data set. To reduce the computation time, parallel implementation is developed and its efficiency is empirically evaluated. Since some of the implemented tests have not been compared in terms of size control and power yet, appropriate simulations are also conducted. Their results can help in choosing proper testing procedures in practice.
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Hlávka, Zdeněk; Hlubinka, Daniel: Functional two-sample tests based on empirical characteristic functionals (2020)
- Mrkvička, Tomáš; Myllymäki, Mari; Jílek, Milan; Hahn, Ute: A one-way ANOVA test for functional data with graphical interpretation. (2020)
- Aneiros, Germán (ed.); Cao, Ricardo (ed.); Vieu, Philippe (ed.): Editorial on the special issue on functional data analysis and related topics (2019)
- Górecki, Tomasz; Smaga, Łukasz: fdANOVA: an R software package for analysis of variance for univariate and multivariate functional data (2019)