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However, most existing region\u2010based methods characterize methylation dysregulation primarily through changes in mean methylation levels associated with a phenotype of interest. Substantial evidence indicates that phenotype\u2010associated methylation alterations may also manifest through changes in methylation variability or through joint shifts in mean and variability. Despite this, no existing statistical framework jointly models mean\u2013variance methylation changes in a region\u2010based manner. We propose ReMeDy, a flexible statistical framework that uses a hierarchical likelihood approach within a generalized linear model setting to identify differentially methylated regions, variably methylated regions, and regions exhibiting joint differential and variable methylation at a genome\u2010wide scale. Unlike existing models, ReMeDy operates directly on biologically defined co\u2010methylated regions, allowing it to naturally capture spatial correlation inherent in DNA methylation array data, while avoiding reliance on heuristic, user\u2010defined tuning parameters such as smoothing spans and kernel bandwidths that can substantially influence results and introduce subjectivity. Through extensive simulation studies and comprehensive benchmarking against popular models, we demonstrate that ReMeDy maintains false discovery and Type\u2010I error rates at nominal levels while achieving consistently higher statistical power across a wide range of realistic scenarios. Application to population\u2010level DNA methylation data further shows that ReMeDy identifies biologically meaningful regions and pathways implicated in complex human diseases that are not captured by conventional mean\u2010based analyses alone. ReMeDy is implemented as an open\u2010source R package and is freely available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/SChatLab\/ReMeDy\">https:\/\/github.com\/SChatLab\/ReMeDy<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1002\/sim.70716","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T07:17:43Z","timestamp":1787728663000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>ReMeDy<\/scp>\n                    : A Flexible Statistical Framework for Region\u2010Based Detection of\n                    <scp>DNA<\/scp>\n                    Methylation 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