Identify and rank CpG DNA methylation conservation along the human 
    genome. Specifically it includes bootstrapping methods to provide ranking 
    which should adjust for the differences in length as without it short 
    regions tend to get higher conservation scores.

DNA methylation is an epigenetic modification involved in genomic stability,
      gene regulation, development and disease.
      DNA methylation occurs mainly through the addition of a methyl group to cytosines, 
      for example to cytosines in a CpG dinucleotide context (CpG stands for a cytosine followed by a guanine).
      Tissue-specific methylation patterns lead to genomic regions with different characteristic
      methylation levels.
      E.g. in vertebrates CpG islands (regions with high CpG content) that are associated to promoter regions of
      expressed genes tend to be unmethylated.
      'MethEvolSIM' is a model-based simulation software for the generation and modification
      of cytosine methylation patterns along a given tree, which can be a genealogy of
      cells within an organism, a coalescent tree of DNA sequences sampled from a population, or a species tree.
      The simulations are based on an extension of the model of
      Grosser & Metzler (2020) <doi:10.1186/s12859-020-3438-5> and allows for changes of
      the methylation states at single cytosine positions as well as simultaneous changes of methylation
      frequencies in genomic structures like CpG islands.

Implements FUSE (Functional Segmentation of DNA methylation data), a data-driven method 
    for identifying spatially coherent DNA methylation segments from 
    whole-genome bisulfite sequencing (WGBS) count data. The method performs 
    hierarchical clustering of CpG sites based on methylated and unmethylated 
    read counts across multiple samples and determines the optimal number of 
    segments using an information criterion (AIC or BIC). Resulting segments 
    represent regions with homogeneous methylation profiles across the input 
    cohort while allowing sample-specific methylation levels. The package 
    provides functions for clustering, model selection, tree cutting, 
    segment-level summarization, and visualization. Input can be supplied as 
    count matrices or extracted directly from 'BSseq' and 'methrix' objects.