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betaclust

The goal of betaclust is to appositely model the beta-valued cytosine-guanine dinucleotide (CpG) sites, to objectively identify methylation state thresholds and to identify the differentially methylated CpG (DMC) sites using a model-based clustering approach. The family of BMMs employs different parameter constraints applicable to different study settings. The EM algorithm is used for parameter estimation, with a novel approximation during the M-step providing tractability and ensuring computational feasibility.

Installation

You can install the development version of betaclust like so:

library(devtools)
install_github('koyelucd/betaclust',force = TRUE)
library(betaclust)

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Version

Install

install.packages('betaclust')

Monthly Downloads

218

Version

1.0.5

License

GPL-3

Maintainer

Koyel Majumdar

Last Published

September 19th, 2025

Functions in betaclust (1.0.5)

legacy.data

MethylationEPIC manifest data.
pca.methylation.data

DNA methylation data from patients with prostate cancer
em_icl

Integrated Complete-data Likelihood (ICL) Criterion
beta_kn

Fit the KN. model
em_aic

Akaike Information Criterion
beta_kr

Fit the K.R Model
ecdf.betaclust

The empirical cumulative distribution function plot
betaclust

The betaclust wrapper function
DMC_identification

The DMC identification function
AUC_WD_metric

AUC and WD function
em_bic

Bayesian Information Criterion
beta_k

Fit the K.. model
plot.betaclust

Plots for visualizing the betaclust class object
threshold

Thresholds under the K.. and the KN. models
summary.betaclust

Summarizing the beta mixture model fits