Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC)
         framework for implementing Metropolis-Hastings algorithm in a modular way
         allowing users to specify automatic convergence checker, personalized
         transition kernels, and out-of-the-box multiple MCMC chains using
         parallel computing. Most of the methods implemented in this package can
         be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods
         included, we have: Haario (2001) <doi:10.1007/s11222-011-9269-5>
         Adaptive Metropolis, Vihola (2012) <doi:10.1007/s11222-011-9269-5>
         Robust Adaptive Metropolis, and Thawornwattana et
         al. (2018) <doi:10.1214/17-BA1084> Mirror transition kernels.

Multidimensional scaling (MDS) functions for various tasks that are beyond the beta stage and way past the alpha stage.
             Currently, options are available for weights, restrictions, classical scaling or principal coordinate analysis, transformations (linear, power, Box-Cox, spline, ordinal), outlier mitigation (rdop), out-of-sample estimation (predict), negative dissimilarities, fast and faster executions with low memory footprints, penalized restrictions, cross-validation-based penalty selection, supplementary variable estimation (explain), additive constant estimation, mixed measurement level distance calculation, restricted classical scaling, etc. More will come in the future.
             References. Busing (2024) "A Simple Population Size Estimator for Local Minima Applied to Multidimensional Scaling". Manuscript submitted for publication.
             Busing (2025) "Node Localization by Multidimensional Scaling with Iterative Majorization". Manuscript submitted for publication.
             Busing (2025) "Faster Multidimensional Scaling". Manuscript in preparation.
             Barroso and Busing (2025) "e-RDOP, Relative Density-Based Outlier Probabilities, Extended to Proximity Mapping". Manuscript submitted for publication.