Estimation of multivariate differences between two groups (e.g., multivariate sex differences) with regularized regression methods and predictive approach. See Lönnqvist & Ilmarinen (2021) <doi:10.1007/s11109-021-09681-2> and Ilmarinen et al. (2023) <doi:10.1177/08902070221088155>.
    Includes tools that help in understanding difference score reliability, predictions of difference score variables, conditional intra-class correlations, and heterogeneity of variance estimates. Package development was supported by the Academy of Finland research grant 338891.

Code to support a systems biology research program from inception through publication.  The methods focus on dimension reduction approaches to detect patterns in complex, multivariate experimental data and places an emphasis on informative visualizations.  The goal for this project is to create a package that will evolve over time, thereby remaining relevant and reflective of current methods and techniques.  As a result, we encourage suggested additions to the package, both methodological and graphical.

Partition a data frame across multiple worker processes to
    provide simple multicore parallelism.

An implementation for multivariate functional additive mixed
    models (multiFAMM), see Volkmann et al. (2021, <arXiv:2103.06606>). It builds on developed methods for univariate sparse 
    functional regression models and multivariate functional principal component
    analysis. This package contains the function to run a multiFAMM and some
    convenience functions useful when working with large models. An additional 
    package on GitHub contains more convenience functions to reproduce the 
    analyses of the corresponding paper 
    (<https://github.com/alexvolkmann/multifammPaper>).

The provided package implements multiple contrast tests for functional data (Munko et al., 2023, <arXiv:2306.15259>). These procedures enable us to evaluate the overall hypothesis regarding equality, as well as specific hypotheses defined by contrasts. In particular, we can perform post hoc tests to examine particular comparisons of interest. Different experimental designs are supported, e.g., one-way and multi-way analysis of variance for functional data.