Includes the 100 datasets simulated by Congreve and Lamsdell (2016)
  <doi:10.1111/pala.12236>, and analyses of the partition and quartet distance of
  reconstructed trees from the generative tree, as analysed by Smith (2019)
  <doi:10.1098/rsbl.2018.0632>.

Integrates two numerical omics data sets from the same samples using partial correlations. The output can be represented as a network, bipartite graph or a hypergraph structure. The method used in the package refers to Klaus et al (2021) <doi:10.1016/j.molmet.2021.101295>.

Geometric circle fitting with Levenberg-Marquardt (a, b, R), Levenberg-Marquardt reduced (a, b), Landau, Spath and Chernov-Lesort. Algebraic circle fitting with Taubin, Kasa, Pratt and Fitzgibbon-Pilu-Fisher. Geometric ellipse fitting with ellipse LMG (geometric parameters) and conic LMA (algebraic parameters). Algebraic ellipse fitting with Fitzgibbon-Pilu-Fisher and Taubin.

Provides a set of tools that can be used across 'data.frame' and
    'imputationList' objects.

This is a simple R package that allows to measure the stated preferences using traditional conjoint analysis method.

Implementation of a procedure---Domingue (2012) <https://eric.ed.gov/?id=ED548657>, Domingue (2014) <doi:10.1007/s11336-013-9342-4>; see also Karabatsos (2001) <https://psycnet.apa.org/record/2002-01665-005> and Kyngdon (2011) <doi:10.1348/2044-8317.002004>---to test the single and double cancellation axioms of conjoint measure in data that is dichotomously coded and measured with error.

Generates synthetic data distributions to enable testing various modelling techniques in ways that real data does not allow. Noise can be added in a controlled manner such that the data seems real. This methodology is generic and therefore benefits both the academic and industrial research.

Functions calculating Conley (1999) <doi:10.1016/S0304-4076(98)00084-0> standard errors. The package started by merging and extending multiple packages and 
  other published scripts on this econometric technique. It strongly emphasizes computational optimization. Details are available in the function documentation and in 
  the vignette.