A leadership-inference framework for multivariate time series. The framework for multiple-faction-leadership inference from coordinated activities or 'mFLICA' uses a notion of a leader as an individual who initiates collective patterns that everyone in a group follows. Given a set of time series of individual activities, our goal is to identify periods of coordinated activity, find factions of coordination if more than one exist, as well as identify leaders of each faction. For each time step, the framework infers following relations between individual time series, then identifying a leader of each faction whom many individuals follow but it follows no one. A faction is defined as a group of individuals that everyone follows the same leader. 'mFLICA' reports following relations, leaders of factions, and members of each faction for each time step. Please see Chainarong Amornbunchornvej and Tanya Berger-Wolf (2018) <doi:10.1137/1.9781611975321.62> for methodology and Chainarong Amornbunchornvej (2021) <doi:10.1016/j.softx.2021.100781> for software when referring to this package in publications.

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>.

Multivariable Fractional Polynomial algorithm for model-building. 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 are Royston and Altman (1994) <doi:10.2307/2986270> and Royston and Sauerbrei (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.