Multi-core replication function to make it easier to do fast 
  Monte Carlo simulation. Based on the mcreplicate() function from the 
  'rethinking' package. The 'rethinking' package requires installing 'rstan', 
  which is onerous to install, while also not adding capabilities to this 
  function.

Regression methods to quantify the relation between two measurement
    methods are provided by this package. In particular it addresses regression
    problems with errors in both variables and without repeated measurements. It
    implements the Clinical Laboratory Standard International (CLSI) 
    recommendations (see J. A. Budd et al. 
    (2018, <https://clsi.org/standards/products/method-evaluation/documents/ep09/>) 
    for analytical method comparison and bias estimation using patient samples.
    Furthermore, algorithms for Theil-Sen and equivariant Passing-Bablok estimators 
    are implemented, see F. Dufey (2020, <doi:10.1515/ijb-2019-0157>) and 
    J. Raymaekers and F. Dufey (2022, <arXiv:2202:08060>).
    Further the robust M-Deming and MM-Deming (experimental) are available, see
    G. Pioda (2021, <arXiv:2105:04628>).
    A comprehensive overview over the implemented methods and references can be found 
    in the manual pages 'mcrPioda-package' and 'mcreg'.

Perform the Model Confidence Set procedure of Hansen et.al (2011).

Generates replicated sets of sequences with Monte Carlo simulated timing changes and computes various indicators for evaluating effects of timing uncertainty on sequence analysis results. See Ritschard, G. and Liao, T.F. (2026): "Assessing the Impact of Timing Errors in Sequence Analysis". International Journal of Social Research Methodology <doi:10.1080/13645579.2026.2666297>.

Identifies the optimal number of clusters by calculating the similarity between
             two clustering methods at the same number of clusters using the corrected indices of Rand and
             Jaccard as described in Albatineh and Niewiadomska-Bugaj (2011). The number of clusters at
             which the index attain its maximum more frequently is a candidate for being the optimal
             number of clusters.