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message: 'To cite package "SDGLM" in publications use:'
type: software
license: MIT
title: 'SDGLM: Scalable Bayesian Inference for Dynamic Generalized Linear Models'
version: 0.4.0
doi: 10.32614/CRAN.package.SDGLM
abstract: Implements scalable Markov chain Monte Carlo (Sca-MCMC) algorithms for Bayesian
  inference in dynamic generalized linear models (DGLMs). The package supports Pareto-type
  and Gamma-type DGLMs, which are suitable for modeling heavy-tailed phenomena such
  as wealth allocation and financial returns. It provides simulation tools for synthetic
  DGLM data, adaptive mutation-rate strategies (ScaI, ScaII, ScaIII), geometric temperature
  ladders for parallel tempering, and posterior predictive evaluation metrics (e.g.,
  R2, RMSE). The methodology is based on the scalable MCMC framework described in
  Guo et al. (2025).
authors:
- family-names: Guo
  given-names: Guangbao
  email: ggb11111111@163.com
- family-names: Wen
  given-names: X. Meggie
- family-names: Zhu
  given-names: Lixing
repository: https://guangbaog.r-universe.dev
commit: 22b1b91d817b8227304defcf794568beb0fc68d4
date-released: '2026-01-20'
contact:
- family-names: Guo
  given-names: Guangbao
  email: ggb11111111@163.com