Calculate the maximal fat oxidation, the exercise intensity that elicits the 
              maximal fat oxidation and the SIN model to represent the fat oxidation kinetics. 
              Three variables can be obtained from the SIN model: dilatation, symmetry and translation. 
              Examples of these methods can be found in Montes de Oca et al (2021) <doi:10.1080/17461391.2020.1788650>
              and Chenevière et al. (2009) <doi:10.1249/MSS.0b013e31819e2f91>.

Fractional polynomials are used to represent curvature in regression models. A key reference is Royston and Altman, 1994.

Multivariable fractional polynomial algorithm simultaneously selects variables and functional forms in both generalized linear models and Cox proportional hazard models. Key references for this algorithm are Royston and Altman (1994)<doi:10.2307/2986270> and Sauerbrei and Royston (2008, ISBN:978-0-470-02842-1). In addition, it can model a 'sigmoid' relationship between variable x and an outcome variable y using the approximate cumulative distribution transformation proposed by Royston (2014) <doi:10.1177/1536867X1401400206>. This feature distinguishes it from a standard fractional polynomial function, which lacks the ability to achieve such modeling.

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>.