Performs multiple comparison procedures on curve observations among different treatment groups. The methods are applicable in a variety of situations (such as independent groups with equal or unequal sample sizes, or repeated measures) by using parametric bootstrap. References to these procedures can be found at Konietschke, Gel, and Brunner (2014) <doi:10.1090/conm/622/12431> and Westfall (2011) <doi:10.1080/10543406.2011.607751>.

Data recorded as paths or trajectories may be suitably described 
  by curves, which are independent of their parametrization. For the space of 
  such curves, the package provides functionalities for reading curves, sampling 
  points on curves, calculating distance between curves and for computing Tukey
  curve depth of a curve w.r.t. to a bundle of curves. For details see 
  Lafaye De Micheaux, Mozharovskyi, and Vimond (2021) <doi:10.48550/arXiv.1901.00180>.

Filtering, also known as gating, of flow cytometry samples using 
             the curvHDR method, which is described in Naumann, U., Luta, G. and 
             Wand, M.P. (2010) <DOI:10.1186/1471-2105-11-44>.

Automatic specification and estimation of reserve demand curves for central bank operations. The package can help to choose the best demand curve and identify additional explanatory variables. Various plot and predict options are included. For more details, see Chen et al. (2023) <https://www.imf.org/en/Publications/WP/Issues/2023/09/01/Modeling-the-Reserve-Demand-to-Facilitate-Central-Bank-Operations-538754>.

Cobb's maximum likelihood method for cusp-catastrophe modeling
        (Grasman, van der Maas, and Wagenmakers (2009) <doi:10.18637/jss.v032.i08>;
        Cobb (1981), Behavioral Science, 26(1), 75-78). Includes a cusp() function for model 
        fitting, and several utility functions for plotting, and for comparing the
        model to linear regression and logistic curve models.

Use the high-precision arithmetic provided by the R package 'Rmpfr' 
    to compute a custom-made Gauss quadrature nodes and weights, 
    with up to 33 nodes, using a moment-based method via moment 
    determinants. Paul Kabaila (2022) <arXiv:2211.04729>.