In the big data setting, working data sets are often distributed on multiple machines. However, classical statistical methods are often developed to solve the problems of single estimation or inference. We employ a novel parallel quasi-likelihood method in generalized linear models, to make the variances between different sub-estimators relatively similar. Estimates are obtained from projection subsets of data and later combined by suitably-chosen unknown weights. The philosophy of the package is described in Guo G. (2020) <doi:10.1007/s00180-020-00974-4>.

Bayesian regularized quantile regression utilizing two major classes of shrinkage priors 
    (the spike-and-slab priors and the horseshoe family of priors) leads to efficient Bayesian 
    shrinkage estimation, variable selection and valid statistical inference. In this package, 
    we have implemented robust Bayesian variable selection with spike-and-slab priors under 
    high-dimensional linear regression models (Fan et al. (2024) <doi:10.3390/e26090794> and  
    Ren et al. (2023) <doi:10.1111/biom.13670>), and regularized quantile varying
    coefficient models (Zhou et al.(2023) <doi:10.1016/j.csda.2023.107808>). In particular, 
    valid robust Bayesian inferences under both models in the presence of heavy-tailed errors
    can be validated on finite samples. Additional models with spike-and-slab priors include 
    robust Bayesian group LASSO and robust binary Bayesian LASSO (Fan and Wu (2025) 
    <doi:10.1002/sta4.70078>). Besides, robust sparse Bayesian regression with the horseshoe
    family of (horseshoe, horseshoe+ and regularized horseshoe) priors has also been implemented
    and yielded valid inference results under heavy-tailed model errors(Fan et al.(2025) 
    <doi:10.48550/arXiv.2507.10975>). The Markov chain Monte Carlo (MCMC) algorithms of 
    the proposed and alternative models are implemented in C++.

Quantile regression with fixed effects is a general model for longitudinal data. Here we proposed to solve it by several methods. The estimation methods include three loss functions as check, asymmetric least square and asymmetric Huber functions; and three structures as simple regression, fixed effects and fixed effects with penalized intercepts by LASSO.