A toolbox to derive flexible cutoffs for fit indices in
    'Covariance-based Structural Equation Modeling' based on the paper by
    'Niemand & Mai (2018)' <doi:10.1007/s11747-018-0602-9>. Flexible
    cutoffs are an alternative to fixed cutoffs - rules-of-thumb -
    regarding an appropriate cutoff for fit indices such as 'CFI' or
    'SRMR'. It has been demonstrated that these flexible cutoffs perform
    better than fixed cutoffs in grey areas where misspecification is not
    easy to detect. The package provides an alternative to the tool at
    <https://flexiblecutoffs.org> as it allows to tailor flexible cutoffs
    to a given dataset and model, which is so far not available in the
    tool. The package simulates fit indices based on a given dataset and
    model and then estimates the flexible cutoffs. Some useful functions,
    e.g., to determine the 'GoF-' or 'BoF-nature' of a fit index, are
    provided. So far, additional options for a relative use (is a model
    better than another?) are provided in an exploratory manner.

Provides a  collection of functions to 
	manage, to investigate and to analyze bivariate financial returns by  
	Copulae. Included are the families of Archemedean, Elliptical, 
	Extreme Value, and Empirical Copulae.

A function composition operator to chain a series of calls into a single function, mimicking the math notion of (f o g o h)(x) = h(g(f(x))). Inspired by 'pipeOp' ('|>') since R4.1 and 'magrittr pipe' ('%>%'), the operator build a pipe without putting data through, which is best for anonymous function accepted by utilities such as apply() and lapply().

Over sixty clustering algorithms are provided in this package with consistent input and output, which enables the user to try out algorithms swiftly. Additionally, 26 statistical approaches for the estimation of the number of clusters as well as the mirrored density plot (MD-plot) of clusterability are implemented. The packages is published in Thrun, M.C., Stier Q.: "Fundamental Clustering Algorithms Suite" (2021), SoftwareX, <DOI:10.1016/j.softx.2020.100642>. Moreover, the fundamental clustering problems suite (FCPS) offers a variety of clustering challenges any algorithm should handle when facing real world data, see Thrun, M.C., Ultsch A.: "Clustering Benchmark Datasets Exploiting the Fundamental Clustering Problems" (2020), Data in Brief, <DOI:10.1016/j.dib.2020.105501>.