Calculate a multivariate functional principal component analysis
    for data observed on different dimensional domains. The estimation algorithm
    relies on univariate basis expansions for each element of the multivariate
    functional data  (Happ & Greven, 2018) <doi:10.1080/01621459.2016.1273115>. 
    Multivariate and univariate functional data objects are
    represented by S4 classes for this type of data implemented in the package
    'funData'. For more details on the general concepts of both packages and a case 
    study, see Happ-Kurz (2020) <doi:10.18637/jss.v093.i05>.

Matrix-Based Flexible Project Planning. This package models, plans, and schedules flexible, such as agile, extreme, and hybrid project plans. The package contains project planning, scheduling, and risk assessment functions. Kosztyan (2022) <doi:10.1016/j.softx.2022.100973>.

Constructs and analyzes optimal row-column designs for
    mixed-level factorial experiments under square and rectangular field
    layouts. For square field layouts, the package implements direct
    common-factor constructions by first forming two component treatment
    arrays, one for each factor or super-factor, and then combining them
    through a symbolic cell-wise product following Gopinath, Parsad and
    Mandal (2018) <doi:10.1080/03610926.2017.1376091>. For rectangular
    field layouts, the package constructs designs by extracting a balanced
    principal block from a mixed-level block design, treating it as the
    principal column, taking the complete treatment set as the principal row,
    and generating the full row-column design by cyclic modular development.
    The package also includes repair utilities for improving disconnected or
    partially connected row-column designs through bounded treatment-swap
    searches while preserving the row-column layout structure. The package
    provides diagnostic tools for connectedness, orthogonal factorial
    structure, balance, estimability, and selected optimality criteria for
    row-column designs.