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license: MIT
title: 'LRQVB: Low Rank Correction Quantile Variational Bayesian Algorithm for Multi-Source
  Heterogeneous Models'
version: 1.0.0
doi: 10.32614/CRAN.package.LRQVB
abstract: 'A Low Rank Correction Variational Bayesian algorithm for high-dimensional
  multi-source heterogeneous quantile linear models. More details have been written
  up in a paper submitted to the journal Statistics in Medicine, and the details of
  variational Bayesian methods can be found in Ray and Szabo (2021) <https://doi.org/10.1080/01621459.2020.1847121>.
  It simultaneously performs parameter estimation and variable selection. The algorithm
  supports two model settings: (1) local models, where variable selection is only
  applied to homogeneous coefficients, and (2) global models, where variable selection
  is also performed on heterogeneous coefficients. Two forms of parameter estimation
  are output: one is the standard variational Bayesian estimation, and the other is
  the variational Bayesian estimation corrected with low-rank adjustment.'
authors:
- family-names: Luo
  given-names: Lu
  email: luolu@stu.ynu.edu.cn
- family-names: Li
  given-names: Huiqiong
repository: https://luluo1999.r-universe.dev
commit: 404cddb1496c331782e13ac44cfbafc935f4cd37
date-released: '2025-10-25'
contact:
- family-names: Luo
  given-names: Lu
  email: luolu@stu.ynu.edu.cn